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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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36 pages, 2470 KiB

Open AccessEditor’s ChoiceReview

The Use of Coagulation–Flocculation for Industrial Colored Wastewater Treatment—(I) The Application of Hybrid Materials

by Carmen Zaharia, Corina-Petronela Musteret and Marius-Alexandru Afrasinei

Appl. Sci. 2024, 14(5), 2184; https://doi.org/10.3390/app14052184 - 5 Mar 2024

Cited by 24 | Viewed by 5065

Polluting species released in industrial-colored effluents contaminate water, degrading its quality and persisting in the aquatic environment; therefore, it must be treated for safe discharge or onsite reuse/recycling to ensure a fresh water supply. This review has the principal goal of facilitating understanding [...] Read more.

Polluting species released in industrial-colored effluents contaminate water, degrading its quality and persisting in the aquatic environment; therefore, it must be treated for safe discharge or onsite reuse/recycling to ensure a fresh water supply. This review has the principal goal of facilitating understanding of some important issues concerning wastewater (WW) treatment systems, mainly based on a coagulation–flocculation step, as follows: (i) the significance of and facilities offered by specialized treatment processes, including the coagulation–flocculation step as a single or associated step (i.e., coagulation–flocculation followed by sedimentation/filtration or air flotation); (ii) the characteristics of industrial-colored WW, especially WW from the textile industry, which can be reduced via the coagulation–flocculation step; (iii) primary and secondary groups of hybrid materials and their characteristics when used as coagulants–flocculants; (iv) the influence of different process operating variables and treatment regimens on the efficiency of the studied treatment step; and (v) the benefits of using hybrid materials in colored WW treatment processes and its future development perspectives. The consulted scientific reports underline the benefits of applying hybrid materials as coagulants–flocculants in colored textile WW treatment, mainly fresh, natural hybrid materials that can achieve high removal rates, e.g., dye and color removal of >80%, heavy metals, COD and BOD of >50%, or turbidity removal of >90%. All of the reported data underline the feasibility of using these materials for the removal of colored polluting species (especially dyes) from industrial effluents and the possibility of selecting the adequate one for a specific WW treatment system. Full article

(This article belongs to the Special Issue Wastewater Treatment Technologies II)

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30 pages, 10289 KiB

Open AccessEditor’s ChoiceArticle

Alternative Fuels for the Marine Sector and Their Applicability for Purse Seiners in a Life-Cycle Framework

by Maja Perčić, Nikola Vladimir, Marija Koričan, Ivana Jovanović and Tatjana Haramina

Appl. Sci. 2023, 13(24), 13068; https://doi.org/10.3390/app132413068 - 7 Dec 2023

Cited by 11 | Viewed by 2780

Fossil fuel combustion is a major source of Greenhouse Gases (GHGs), which cause global warming. To prevent further increases in anthropogenic GHGs, the global community needs to take action in each segment of the economy, including the shipping sector. Among different measures for [...] Read more.

Fossil fuel combustion is a major source of Greenhouse Gases (GHGs), which cause global warming. To prevent further increases in anthropogenic GHGs, the global community needs to take action in each segment of the economy, including the shipping sector. Among different measures for reducing shipping emissions, the most promising one is the replacement of conventional marine fuels with alternatives. According to the International Maritime Organisation’s regulations, ships engaged in international shipping need to reduce their annual emissions by at least 50% by 2050. However, this does not apply to fishing vessels, which are highly dependent on fossil fuels and greatly contribute to air pollution. This paper investigates the environmental footprint of a fishing vessel (purse seiner) through the implementation of various alternative fuels. Within the research, Life-Cycle Assessments (LCAs) and Life-Cycle Cost Assessments (LCCAs) are performed, resulting in life-cycle emissions and lifetime costs for each alternative, which are then compared to a diesel-powered ship (baseline scenario). The comparison, based on environmental and economic criteria, highlighted methanol as the most suitable alternative for the purse seiner, as its use onboard resulted in 22.4% lower GHGs and 23.3% lower costs in comparison to a diesel-powered ship. Full article

(This article belongs to the Special Issue Advances in Maritime Transport: Sustainability, Contamination and New Technologies)

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<p>Alternative marine fuel uptake in 2022 [<a href="#B10-applsci-13-13068" class="html-bibr">10</a>] (reproduced from [<a href="#B10-applsci-13-13068" class="html-bibr">10</a>] with permission of DNV, 2022).</p> Full article ">Figure 2
<p>Fuels for maritime purposes.</p> Full article ">Figure 3
<p>Carbon taxation scenarios.</p> Full article ">Figure 4
<p>Typical operating profile of a purse seiner.</p> Full article ">Figure 5
<p>Load profile of a purse seiner on a random fishing trip.</p> Full article ">Figure 6
<p>Processes included in the LCA of a diesel-powered ship.</p> Full article ">Figure 7
<p>Processes included in the LCA of an LNG-powered ship.</p> Full article ">Figure 8
<p>Processes included in the LCA of an LPG-powered ship.</p> Full article ">Figure 9
<p>Processes included in the LCA of a methanol-powered ship.</p> Full article ">Figure 10
<p>Processes included in the LCA of a DME-powered ship.</p> Full article ">Figure 11
<p>Processes included in the LCA of a B20-powered ship.</p> Full article ">Figure 12
<p>Processes included in the LCA of a B100-powered ship.</p> Full article ">Figure 13
<p>Processes included in the LCA of an LBG-powered ship.</p> Full article ">Figure 14
<p>Processes included in the LCA of a hydrogen-powered ship (FC-H).</p> Full article ">Figure 15
<p>Processes included in the LCA of an ammonia-powered ship (FC-A).</p> Full article ">Figure 16
<p>Processes included in the LCA of an ammonia-powered ship (ICE-A).</p> Full article ">Figure 17
<p>Processes included in the LCA of an all-electric ship.</p> Full article ">Figure 18
<p>LCA comparison of different power systems (impact category: climate change).</p> Full article ">Figure 19
<p>LCA comparison of different power systems (impact category: acidification).</p> Full article ">Figure 20
<p>LCA comparison of different power systems (impact category: human toxicity).</p> Full article ">Figure 21
<p>LCCA results.</p> Full article ">Figure 22
<p>Sensitivity analysis.</p> Full article ">

26 pages, 21046 KiB

Open AccessEditor’s ChoiceArticle

Refined Landslide Susceptibility Mapping by Integrating the SHAP-CatBoost Model and InSAR Observations: A Case Study of Lishui, Southern China

by Zhaowei Yao, Meihong Chen, Jiewei Zhan, Jianqi Zhuang, Yuemin Sun, Qingbo Yu and Zhaoyue Yu

Appl. Sci. 2023, 13(23), 12817; https://doi.org/10.3390/app132312817 - 29 Nov 2023

Cited by 12 | Viewed by 1918

Landslide susceptibility mapping based on static influence factors often exhibits issues of low accuracy and classification errors. To enhance the accuracy of susceptibility mapping, this study proposes a refined approach that integrates categorical boosting (CatBoost) with small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) [...] Read more.

Landslide susceptibility mapping based on static influence factors often exhibits issues of low accuracy and classification errors. To enhance the accuracy of susceptibility mapping, this study proposes a refined approach that integrates categorical boosting (CatBoost) with small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) results, achieving more precise and detailed susceptibility mapping. We utilized optical remote sensing images, the information value (IV) model, and fourteen influencing factors (elevation, slope, aspect, roughness, profile curvature, plane curvature, lithology, distance to faults, land use type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), distance to rivers, distance to roads, and annual precipitation) to establish the IV-CatBoost landslide susceptibility mapping method. Subsequently, the Sentinel-1A ascending data from January 2021 to March 2023 were utilized to derive the deformation rates within the city of Lishui in the southern region of China. Based on the outcomes derived from IV-CatBoost and SBAS-InSAR, a discernment matrix was formulated to rectify inaccuracies in the partitioned regions, leading to the creation of a refined information value CatBoost integration (IVCI) landslide susceptibility mapping model. In the end, we utilized optical remote sensing interpretations alongside surface deformations obtained from SBAS-InSAR to cross-verify the excellence and accuracy of IVCI. Research findings indicate a distinct enhancement in susceptibility levels across 165,784 grids (149.20 km²) following the integration of SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images closely correspond to the trends of SBAS-InSAR cumulative deformation, reflecting a high level of consistency with field-based conditions. These improved classifications effectively enhance the refinement of landslide susceptibility mapping. The refined susceptibility mapping approach proposed in this paper effectively enhances landslide prediction accuracy, providing valuable technical reference for landslide hazard prevention and control in the Lishui region. Full article

(This article belongs to the Special Issue Remote Sensing Technology in Landslide and Land Subsidence)

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<p>Map of the study area and the distribution of landslide points. (<b>a</b>) The geographical location of the study area; (<b>b</b>) Elevation map of the city of Lishui with landslide locations.</p> Full article ">Figure 2
<p>Optical remote sensing interpretation results of landslides in the city of Lishui using Google Earth imagery.</p> Full article ">Figure 3
<p>Research workflow diagram.</p> Full article ">Figure 4
<p>Temporal and spatial baselines of the SAR datasets employed in this study. (<b>a</b>) Time–position plot illustrating InSAR image acquisitions; (<b>b</b>) Time–baseline plot depicting interference pairs of SAR images.</p> Full article ">Figure 5
<p>Impact factor correlation heat map (PlaneC: plane curvature; ProfC: profile curvature; Rough: roughness; Litho: lithology; LandU: land use; DTF: distance to faults; DRI: distance to rivers; DTR: distance to roads; AnnualP: annual precipitation).</p> Full article ">Figure 6
<p>Landslide susceptibility map of IV: regions with very low and low susceptibility, and the distribution of non-landslide points.</p> Full article ">Figure 7
<p>Landslide susceptibility maps of the (<b>a</b>) IV-CatBoost and (<b>b</b>) IVCI models.</p> Full article ">Figure 8
<p>IV-CatBoost model ROC curve. An AUC value ranging from 0.7 to 0.9 indicates favorable model performance, signifying its heightened classification capability.</p> Full article ">Figure 9
<p>Map of SBAS-InSAR annual subsidence rates.</p> Full article ">Figure 10
<p>Map of the post-correction level differences between the IV-CatBoost and IVCI model.</p> Full article ">Figure 11
<p>Comparison of landslide susceptibility for Y-1 under the (<b>a</b>) IV-CatBoost model and (<b>b</b>) IVCI model.</p> Full article ">Figure 12
<p>Y-1 optical remote sensing interpretation. (<b>1</b>~<b>9</b>), delineated by the red lines, are all classified as shallow surface landslides. The images (<b>a</b>,<b>b</b>) are both derived from Maxar satellite data.</p> Full article ">Figure 13
<p>The SBAS-InSAR results within Y-1. (<b>a</b>) Velocity map of the Y-1 SBAS-InSAR results; (<b>b</b>) map of P-1 cumulative settlement.</p> Full article ">Figure 14
<p>Comparison of landslide susceptibility for Y-2 under the (<b>a</b>) IV-CatBoost model and (<b>b</b>) IVCI model.</p> Full article ">Figure 15
<p>Y-2 optical remote sensing interpretation. (<b>1</b>~<b>8</b>), delineated by the red lines, are all classified as shallow surface landslides. (<b>a</b>) image from Maxar Technologies, (<b>b</b>) image from CNES/Airbus.</p> Full article ">Figure 16
<p>The SBAS-InSAR results within Y-2. (<b>a</b>) Velocity map of the Y-2 SBAS-InSAR results; (<b>b</b>) map of P-2 cumulative settlement.</p> Full article ">Figure 17
<p>Comparison of landslide susceptibility for Y-3 under the (<b>a</b>) IV-CatBoost model and (<b>b</b>) IVCI model.</p> Full article ">Figure 18
<p>Y-3 optical remote sensing interpretation. (<b>1</b>~<b>4</b>), delineated by the red lines, are all classified as shallow surface landslides. Both (<b>5</b>) and (<b>6</b>) are characterized as bare rock. Images (<b>a</b>,<b>b</b>) are both derived from CNES/Airbus satellite data.</p> Full article ">Figure 19
<p>The SBAS-InSAR results within Y-3. (<b>a</b>) Velocity map of the Y-3 SBAS-InSAR results; (<b>b</b>) map of P-3 cumulative settlement.</p> Full article ">Figure 20
<p>IVCI model ROC curve. An AUC value exceeding 0.9 signifies exceptional model performance, demonstrating outstanding classification capability within the model.</p> Full article ">Figure 21
<p>Map of the results of the feature importance analysis. (<b>a</b>) Feature importance ranking; (<b>b</b>) SHAP summary plot of the test dataset.</p> Full article ">Figure 22
<p>Distribution of predicted values across (<b>a</b>) elevation, (<b>b</b>) slope, (<b>c</b>) roughness, and (<b>d</b>) land use.</p> Full article ">

33 pages, 5128 KiB

Open AccessEditor’s ChoiceReview

Mineral Characterization Using Scanning Electron Microscopy (SEM): A Review of the Fundamentals, Advancements, and Research Directions

by Asif Ali, Ning Zhang and Rafael M. Santos

Appl. Sci. 2023, 13(23), 12600; https://doi.org/10.3390/app132312600 - 22 Nov 2023

Cited by 39 | Viewed by 27599

Scanning electron microscopy (SEM) is a powerful tool in the domains of materials science, mining, and geology owing to its enormous potential to provide unique insight into micro and nanoscale worlds. This comprehensive review discusses the background development of SEM, basic SEM operation, [...] Read more.

Scanning electron microscopy (SEM) is a powerful tool in the domains of materials science, mining, and geology owing to its enormous potential to provide unique insight into micro and nanoscale worlds. This comprehensive review discusses the background development of SEM, basic SEM operation, including specimen preparation and image processing, and the fundamental theoretical calculations underlying SEM operation. It provides a foundational understanding for engineers and scientists who have never had a chance to dig in depth into SEM, contributing to their understanding of the workings and development of this robust analytical technique. The present review covers how SEM serves as a crucial tool in mineral characterization, with specific discussion on the workings and research fronts of SEM-EDX, SEM-AM, SEM-MLA, and QEMSCAN. With automation gaining pace in the development of all spheres of technology, understanding the uncertainties in SEM measurements is very important. The constraints in mineral phase identification by EDS spectra and sample preparation are conferred. In the end, future research directions for SEM are analyzed with the possible incorporation of machine learning, deep learning, and artificial intelligence tools to automate the process of mineral identification, quantification, and efficient communication with researchers so that the robustness and objectivity of the analytical process can be improved and the analysis time and involved costs can be reduced. This review also discusses the idea of integrating robotics with SEM to make the equipment portable so that further mineral characterization insight can be gained not only on Earth but also on other terrestrial grounds. Full article

(This article belongs to the Special Issue Scanning Electron Microscopy and Energy-Dispersive Spectroscopy Analyses in Materials Science)

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<p>Various possible interactions of high-energy electrons with atoms. The atomic shells are labeled with standard notation (i.e., K, L, M). The incident particle is shown with a solid arrow. (<b>a</b>) Low-angle scattering: very little energy loss is experienced by the incident electrons and they scatter to the next layer of atoms; (<b>b</b>) high-angle (or back) scattering; (<b>c</b>) emission of characteristic X-rays and a secondary electron; (<b>d</b>) emission of an Auger electron and a secondary electron.</p> Full article ">Figure 2
<p>Various levels of electron penetration through the sample surface.</p> Full article ">Figure 3
<p>(<b>Top</b>): Ilmenite micrographs at various magnification levels (note: scale bars are correct, magnification values are incorrect), aimed at analyzing the existing (<b>a</b>) cracks, (<b>b</b>) furrows, and (<b>c</b>) particle shape in the sample [<a href="#B50-applsci-13-12600" class="html-bibr">50</a>]; CC-BY. (<b>Bottom</b>): Aspects of monazite crystals: (<b>a</b>) colloform, (<b>b</b>) acicular, (<b>c</b>) massive, and (<b>d</b>) as micrometric aggregates, where Mnz = monazite and Mag = magnetite [<a href="#B8-applsci-13-12600" class="html-bibr">8</a>]; CC BY-NC-ND with permission (5672760157749) from Elsevier.</p> Full article ">Figure 4
<p>BSE image of the thin section of a rock sample (<b>top left</b>) and artificial color-scaled quantitative mean atomic number image (<b>top right</b>) [<a href="#B56-applsci-13-12600" class="html-bibr">56</a>]; re-used with permission (5672740688619) from Oxford University Press. A 20 keV cross-section BSE image of the In<span class="html-italic"><sub>x</sub></span>Ga<sub>1−<span class="html-italic">x</span></sub>As/GaAs-heterostructure from a specimen with wedge-shaped thickness profile (<b>bottom</b>) [<a href="#B57-applsci-13-12600" class="html-bibr">57</a>]; re-used with permission (5672741337235) from Elsevier.</p> Full article ">Figure 5
<p>Schematic diagram of an energy-dispersive spectrometer.</p> Full article ">Figure 6
<p>SEM-AM methods of one measurement frame showing BSE (<b>upper row</b>, <b>a</b>–<b>d</b>) and EDS (<b>lower row</b>) images. Numerous single EDS analysis points map each grain with a distinguishable BSE gray level and are visualized as color-coded pixels, such as the garnet grain, which is indicated by red-colored pixels [<a href="#B84-applsci-13-12600" class="html-bibr">84</a>]. CC-BY.</p> Full article ">Figure 7
<p>(<b>a</b>) SEM-MLA measurement of a hydrothermally overprinted alkali plutonite showing the backscattered electron (BSE) image and (<b>b</b>) color-coded, grouped, and classified presentation of the frame presented in (<b>a</b>) [<a href="#B85-applsci-13-12600" class="html-bibr">85</a>]. CC-BY.</p> Full article ">Figure 8
<p>QEMSCAN analysis indicating mineral distribution in four different zones [<a href="#B130-applsci-13-12600" class="html-bibr">130</a>]. CC-BY.</p> Full article ">Figure 9
<p>Classification modes of EDS spectra: (<b>a</b>) FEI-QEMSCAN, and (<b>b</b>) FEI-MLA for feldspar mineral albite [<a href="#B84-applsci-13-12600" class="html-bibr">84</a>]. CC-BY.</p> Full article ">Figure 10
<p>Epoxy adhesives shown using SEM with (<b>a</b>) epoxy resin only, (<b>b</b>) epoxy resin with aluminum nitride particles, (<b>c</b>) epoxy resin with aluminum nitride and graphene oxide, and (<b>d</b>) the thermal conductivities of various test samples. [<a href="#B178-applsci-13-12600" class="html-bibr">178</a>,<a href="#B179-applsci-13-12600" class="html-bibr">179</a>]. Re-used with permission (5673961270857) from Elsevier (<b>a–c</b>) and CC-BY (<b>d</b>).</p> Full article ">Figure 11
<p>Wollastonite samples prepared for SEM analysis: (A) multilayer, coated; (B) single layer, coated; and (C) single layer, uncoated.</p> Full article ">Figure 12
<p>SEM images of wollastonite samples <b>A</b>, <b>B</b>, and <b>C</b> captured at 5k×, 60k×, and 250k× magnifications.</p> Full article ">Figure 13
<p>Comparison of the stigmator adjustment effect on wollastonite SEM images (<b>a</b>) before adjustment (<b>b</b>) after adjustment.</p> Full article ">Figure 14
<p>The effect of electron beam focusing on the sample for a longer period of time at (<b>a</b>) 60k× and (<b>b</b>) 5k× magnifications.</p> Full article ">

40 pages, 1500 KiB

Open AccessEditor’s ChoiceArticle

Soft, Rigid, and Hybrid Robotic Exoskeletons for Hand Rehabilitation: Roadmap with Impairment-Oriented Rationale for Devices Design and Selection

by Gabriele Maria Achilli, Cinzia Amici, Mihai Dragusanu, Massimiliano Gobbo, Silvia Logozzo, Monica Malvezzi, Monica Tiboni and Maria Cristina Valigi

Appl. Sci. 2023, 13(20), 11287; https://doi.org/10.3390/app132011287 - 14 Oct 2023

Cited by 12 | Viewed by 5510

In recent decades, extensive attention has been paid to the study and development of robotic devices specifically designed for hand rehabilitation. Accordingly, a many concepts concerning rigid, soft, and hybrid types have emerged in the literature, with significant ongoing activity being directed towards [...] Read more.

In recent decades, extensive attention has been paid to the study and development of robotic devices specifically designed for hand rehabilitation. Accordingly, a many concepts concerning rigid, soft, and hybrid types have emerged in the literature, with significant ongoing activity being directed towards the development of new solutions. In this context, the paper focuses on the technical features of devices conceived for the robotic rehabilitation of the hand with reference to the three kinds of exoskeleton architecture and the clinical requirements demanded by the target impairment of the end-user. The work proposes a roadmap (i) for both the design and selection of exoskeletons for hand rehabilitation, (ii) to discriminate among the peculiarities of soft, rigid, and hybrid devices, and (iii) with an impairment-oriented rationale. The clinical requirements expected for an exoskeleton are identified by applying a PICO-inspired approach focused on the impairment analysis; the technical features are extracted from a proposed design process for exoskeletons combined with a narrative literature review. A cross-analysis between device families and features is presented to provide a supporting tool for both the design and selection of exoskeletons according to an impairment-oriented rationale. Full article

(This article belongs to the Special Issue Design, Optimization and Performance Analysis of Soft Robots)

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<p>Schematic representation of the three classes of robotic hand rehabilitation exoskeletons: (<b>a</b>) rigid, (<b>b</b>) soft, and (<b>c</b>) hybrid.</p> Full article ">Figure 2
<p>General design flow-chart for a robotic exoskeleton device for hand rehabilitation with the indication of the professional figures responsible for each phase and with the identification of the macro-phases.</p> Full article ">Figure 3
<p>Examples of rigid hand exoskeletons. (<b>a</b>) Modular exoskeleton for finger flexion/extension movement support, actuated by linear actuators [<a href="#B32-applsci-13-11287" class="html-bibr">32</a>]. (<b>b</b>) Hand exoskeleton for assistive and rehabilitative purposes developed by Secciani et al. [<a href="#B44-applsci-13-11287" class="html-bibr">44</a>]. (<b>c</b>) Rigid hand exoskeleton proposed by Esposito et al. [<a href="#B37-applsci-13-11287" class="html-bibr">37</a>].</p> Full article ">Figure 4
<p>Examples of soft hand exoskeletons. (<b>a</b>) Soft exoskeleton with Pneu-Nets soft actuators. Source: Tiboni et al., 2023 [<a href="#B66-applsci-13-11287" class="html-bibr">66</a>]. (<b>b</b>) Example of a fabric-based soft exoskeleton. Source: Cappello et al., 2018 [<a href="#B67-applsci-13-11287" class="html-bibr">67</a>]. (<b>c</b>) Wearable exoskeleton with Machine-Knitted Seamless Pneumatic Actuators. Source: Elmoughni et al., 2021 [<a href="#B68-applsci-13-11287" class="html-bibr">68</a>]. (<b>d</b>) Soft robotic glove with actuators consisting of molded elastomeric chambers with fiber reinforcements. Source: Polygerinos et al., 2015, open manuscript [<a href="#B69-applsci-13-11287" class="html-bibr">69</a>].</p> Full article ">Figure 5
<p>Examples of hybrid hand exoskeletons. (<b>a</b>) The device consists of pneumatic actuation, combined with rigid armor. Source: Kladovasilakis et al. [<a href="#B121-applsci-13-11287" class="html-bibr">121</a>]. (<b>b</b>,<b>c</b>) Global and bottom view, respectively, of a hybrid assistive hand exoskeleton for stroke patience: a rigid structure with flexible joints. Source: Vertongen et al. [<a href="#B127-applsci-13-11287" class="html-bibr">127</a>].</p> Full article ">Figure 6
<p>Color-based representation of soft, rigid, and hybrid soft/rigid hand exoskeleton performance in terms of the design/selection features.</p> Full article ">

39 pages, 4414 KiB

Open AccessEditor’s ChoiceReview

Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities

by Ahmet Çağdaş Seçkin, Bahar Ateş and Mine Seçkin

Appl. Sci. 2023, 13(18), 10399; https://doi.org/10.3390/app131810399 - 17 Sep 2023

Cited by 73 | Viewed by 51775

Wearable technology is increasingly vital for improving sports performance through real-time data analysis and tracking. Both professional and amateur athletes rely on wearable sensors to enhance training efficiency and competition outcomes. However, further research is needed to fully understand and optimize their potential [...] Read more.

Wearable technology is increasingly vital for improving sports performance through real-time data analysis and tracking. Both professional and amateur athletes rely on wearable sensors to enhance training efficiency and competition outcomes. However, further research is needed to fully understand and optimize their potential in sports. This comprehensive review explores the measurement and monitoring of athletic performance, injury prevention, rehabilitation, and overall performance optimization using body wearable sensors. By analyzing wearables’ structure, research articles across various sports, and commercial sensors, the review provides a thorough analysis of wearable sensors in sports. Its findings benefit athletes, coaches, healthcare professionals, conditioners, managers, and researchers, offering a detailed summary of wearable technology in sports. The review is expected to contribute to future advancements in wearable sensors and biometric data analysis, ultimately improving sports performance. Limitations such as privacy concerns, accuracy issues, and costs are acknowledged, stressing the need for legal regulations, ethical principles, and technical measures for safe and fair use. The importance of personalized devices and further research on athlete comfort and performance impact is emphasized. The emergence of wearable imaging devices holds promise for sports rehabilitation and performance monitoring, enabling enhanced athlete health, recovery, and performance in the sports industry. Full article

(This article belongs to the Special Issue Advances in Wearable Devices for Sports)

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30 pages, 12369 KiB

Open AccessEditor’s ChoiceReview

Current Trends in Fluid Viscous Dampers with Semi-Active and Adaptive Behavior

by Luca Zoccolini, Eleonora Bruschi, Sara Cattaneo and Virginio Quaglini

Appl. Sci. 2023, 13(18), 10358; https://doi.org/10.3390/app131810358 - 15 Sep 2023

Cited by 13 | Viewed by 4932

Fluid viscous dampers (FVDs) have shown their efficiency as energy-dissipating systems, reducing the effects induced on structures by dynamic loading conditions like earthquakes and winds. In this paper, the evolution of this technology is reviewed, with a focus on the current trends in [...] Read more.

Fluid viscous dampers (FVDs) have shown their efficiency as energy-dissipating systems, reducing the effects induced on structures by dynamic loading conditions like earthquakes and winds. In this paper, the evolution of this technology is reviewed, with a focus on the current trends in development from passive to semi-active and adaptive systems and an emphasis on their advances in adaptability and control efficacy. The paper examines the implementation of semi-active FVDs such as electrorheological, magnetorheological, variable stiffness, and variable damping dampers. These devices have a high potential to mitigate the vibrations caused by earthquakes of different intensities. In addition, adaptive FVDs are presented. As semi-active devices, the adaptive ones can adjust their behavior according to the dynamic excitations’ intensity; however, they are able to do that autonomously without the use of any external equipment. Full article

(This article belongs to the Section Civil Engineering)

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<p>Effect of energy-dissipating devices on the Acceleration–Displacement Response Spectrum: (<b>a</b>) DDDs; (<b>b</b>) VDDs.</p> Full article ">Figure 2
<p>Scheme of a classical passive FVD [<a href="#B47-applsci-13-10358" class="html-bibr">47</a>].</p> Full article ">Figure 3
<p>Force–displacement curves (<b>a</b>) and force–velocity curves (<b>b</b>) for passive FVDs with different α exponents [<a href="#B57-applsci-13-10358" class="html-bibr">57</a>].</p> Full article ">Figure 4
<p>Model of a viscous dashpot (adapted from [<a href="#B71-applsci-13-10358" class="html-bibr">71</a>]).</p> Full article ">Figure 5
<p>(<b>a</b>) Maxwell model and (<b>b</b>) Kelvin–Voigt model (adapted from [<a href="#B44-applsci-13-10358" class="html-bibr">44</a>]).</p> Full article ">Figure 6
<p>Four configurations of the passive FVDs of Seleemah and Constantinou [<a href="#B54-applsci-13-10358" class="html-bibr">54</a>].</p> Full article ">Figure 7
<p>A Taylor Company FVD [<a href="#B31-applsci-13-10358" class="html-bibr">31</a>].</p> Full article ">Figure 8
<p>(<b>a</b>) 181 Fremont skyscraper, San Francisco; (<b>b</b>) Allianz Tower, Milano [<a href="#B34-applsci-13-10358" class="html-bibr">34</a>].</p> Full article ">Figure 9
<p>(<b>a</b>) The Rion-Antirion Bridge; (<b>b</b>) the seismic protection system of the suspended bridge [<a href="#B93-applsci-13-10358" class="html-bibr">93</a>].</p> Full article ">Figure 10
<p>Behavior of ER and MR fluids (adapted from [<a href="#B46-applsci-13-10358" class="html-bibr">46</a>]).</p> Full article ">Figure 11
<p>ER and MR fluids’ operating modes: (<b>a</b>) flow mode; (<b>b</b>) shear mode; (<b>c</b>) squeeze-flow mode; (<b>d</b>) magnetic gradient pinch mode (adapted from [<a href="#B46-applsci-13-10358" class="html-bibr">46</a>]). N: north pole of the magnetic field, S: south pole of the magnetic field, F: force.</p> Full article ">Figure 12
<p>Schematic example of ER damper [<a href="#B46-applsci-13-10358" class="html-bibr">46</a>].</p> Full article ">Figure 13
<p>ER fluid damper proposed by Makris et al. [<a href="#B118-applsci-13-10358" class="html-bibr">118</a>].</p> Full article ">Figure 14
<p>MR damper developed by Lord Corporation [<a href="#B46-applsci-13-10358" class="html-bibr">46</a>]: (<b>a</b>) section of the device; (<b>b</b>) fluid path in the piston head and coil placement [<a href="#B121-applsci-13-10358" class="html-bibr">121</a>].</p> Full article ">Figure 15
<p>MR damper proposed by Yi et al. [<a href="#B106-applsci-13-10358" class="html-bibr">106</a>].</p> Full article ">Figure 16
<p>(<b>a</b>) Bingham model (adapted from [<a href="#B46-applsci-13-10358" class="html-bibr">46</a>]); (<b>b</b>) force–velocity response of the Bingham model (<math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>670</mn> <mo> </mo> <mi>N</mi> <mo>,</mo> <mo> </mo> <msub> <mi>C</mi> <mi>D</mi> </msub> <mo>=</mo> <mn>50</mn> <mi>N</mi> <mi>s</mi> <mo>/</mo> <mi>cm</mi> <mo>,</mo> <mo> </mo> <msub> <mi>f</mi> <mn>0</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>95</mn> <mo> </mo> <mi>N</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> [<a href="#B129-applsci-13-10358" class="html-bibr">129</a>].</p> Full article ">Figure 17
<p>(<b>a</b>) Bouc–Wen model (adapted from [<a href="#B46-applsci-13-10358" class="html-bibr">46</a>]); (<b>b</b>) force–velocity response of the Bouc–Wen model [<a href="#B129-applsci-13-10358" class="html-bibr">129</a>].</p> Full article ">Figure 18
<p>Model proposed by Spencer et al. [<a href="#B129-applsci-13-10358" class="html-bibr">129</a>]: (<b>a</b>) rheological model (adapted from [<a href="#B129-applsci-13-10358" class="html-bibr">129</a>]); (<b>b</b>) force–velocity response.</p> Full article ">Figure 19
<p>Application of MR dampers: (<b>a</b>) Tokyo National Museum of Emerging Science and Innovation [<a href="#B147-applsci-13-10358" class="html-bibr">147</a>]; (<b>b</b>) Dongting Lake cable-stayed bridge [<a href="#B155-applsci-13-10358" class="html-bibr">155</a>].</p> Full article ">Figure 20
<p>Variable stiffness damper: (<b>a</b>) section of the device [<a href="#B123-applsci-13-10358" class="html-bibr">123</a>]; (<b>b</b>) rheological model of the system composed of the damper and the brace (adapted from [<a href="#B46-applsci-13-10358" class="html-bibr">46</a>]).</p> Full article ">Figure 21
<p>Semi-active FVD equipped with servo-valve [<a href="#B159-applsci-13-10358" class="html-bibr">159</a>,<a href="#B160-applsci-13-10358" class="html-bibr">160</a>].</p> Full article ">Figure 22
<p>Adaptive FVDs with relief valves: (<b>a</b>) geometry scheme [<a href="#B167-applsci-13-10358" class="html-bibr">167</a>]; (<b>b</b>) damper’s pressure–velocity characteristic [<a href="#B168-applsci-13-10358" class="html-bibr">168</a>].</p> Full article ">Figure 23
<p>Adaptive FVDs with relief valves [<a href="#B171-applsci-13-10358" class="html-bibr">171</a>]: (<b>a</b>) device configuration; (<b>b</b>) force–displacement profile.</p> Full article ">Figure 24
<p>Schematic force–displacement hysteresis loop for (<b>a</b>) a standard FVD, (<b>b</b>) 1–3 D3 FVD, and (<b>c</b>) 2–4 D3 FVD [<a href="#B173-applsci-13-10358" class="html-bibr">173</a>].</p> Full article ">Figure 25
<p>First-quadrant passive D3 FVDs [<a href="#B175-applsci-13-10358" class="html-bibr">175</a>].</p> Full article ">Figure 26
<p>The 2–4 passive D3 FVD [<a href="#B175-applsci-13-10358" class="html-bibr">175</a>].</p> Full article ">Figure 27
<p>AVD proposed by Hormozabad and Zahrai [<a href="#B166-applsci-13-10358" class="html-bibr">166</a>]: (<b>a</b>) layout of the AVD; (<b>b</b>) detail of the piston head.</p> Full article ">Figure 28
<p>Three stages of the VCVFD presented by Xu et al. [<a href="#B43-applsci-13-10358" class="html-bibr">43</a>]: (<b>a</b>) 1st stage, (<b>b</b>) 2nd stage, and (<b>c</b>) third stage.</p> Full article ">Figure 29
<p>Force–displacement loops of a VCVFD and a passive FVD (adapted from [<a href="#B43-applsci-13-10358" class="html-bibr">43</a>]).</p> Full article ">

20 pages, 1441 KiB

Open AccessEditor’s ChoiceArticle

Crop Prediction Model Using Machine Learning Algorithms

by Ersin Elbasi, Chamseddine Zaki, Ahmet E. Topcu, Wiem Abdelbaki, Aymen I. Zreikat, Elda Cina, Ahmed Shdefat and Louai Saker

Appl. Sci. 2023, 13(16), 9288; https://doi.org/10.3390/app13169288 - 16 Aug 2023

Cited by 99 | Viewed by 42394

Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the [...] Read more.

Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the potential benefits of integrating machine learning algorithms in modern agriculture. The main focus of these algorithms is to help optimize crop production and reduce waste through informed decisions regarding planting, watering, and harvesting crops. This paper includes a discussion on the current state of machine learning in agriculture, highlighting key challenges and opportunities, and presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, incorporating online IoT sensor data that were obtained in a real-time manner, farmers can make more informed verdicts about factors that affect crop growth. Eventually, integrating these technologies can transform modern agriculture by increasing crop yields while minimizing waste. Fifteen different algorithms have been considered to evaluate the most appropriate algorithms to use in agriculture, and a new feature combination scheme-enhanced algorithm is presented. The results show that we can achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99.46% using Naïve Bayes Classifier and Hoeffding Tree algorithms. These results will indicate an increase in production rates and reduce the effective cost for the farms, leading to more resilient infrastructure and sustainable environments. Moreover, the findings we obtained in this study can also help future farmers detect diseases early, increase crop production efficiency, and reduce prices when the world is experiencing food shortages. Full article

(This article belongs to the Special Issue Advances in Technology Applied in Agricultural Engineering)

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23 pages, 4882 KiB

Open AccessEditor’s ChoiceArticle

Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder

by Ibrahim Haruna Umar, Hang Lin and Awaisu Shafiu Ibrahim

Appl. Sci. 2023, 13(16), 9274; https://doi.org/10.3390/app13169274 - 15 Aug 2023

Cited by 17 | Viewed by 4488

Soil stabilization is a critical step in numerous engineering projects, preventing soil erosion, increasing soil strength, and reducing the risk of subsidence. Due to its inexpensive cost and potential environmental benefits, waste materials, such as waste marble powder (WMP), have been used as [...] Read more.

Soil stabilization is a critical step in numerous engineering projects, preventing soil erosion, increasing soil strength, and reducing the risk of subsidence. Due to its inexpensive cost and potential environmental benefits, waste materials, such as waste marble powder (WMP), have been used as additives for soil stabilization in recent years. This study investigates waste marble powder’s effects on unconfined compressive strength (UCS) and clayey soil’s ultrasonic pulse velocity (UPV) at different water contents and curing times, and artificial neural networks (ANNs) are also used to predict the UCS and UPV values based on three input variables (percentage of waste marble dust, curing time, and moisture content). Geo-engineering experiments (Atterberg limits, compaction characteristics, specific gravity, UCS, and UPV) and analytical methods (ANNs) are used. The study results indicate that the soil is high-plasticity clay (CH) using the Unified Soil Classification System (USCS), and adding waste marble powder (WMP) can significantly improve the UCS and UPV of clay soils, especially at optimal water content, curing times of 28 days, and 60% WMP. It is found that the ANN models accurately predict the UCS and UPV values with high correlation coefficients approaching 1. In addition, this study shows that the optimum water content and curing time for stabilized clay soils depend on the grade and amount of waste marble powder utilized. Overall, the study demonstrates the potential of waste marble dust as a soil stabilization additive and the usefulness of ANNs in predicting UCS and UPV values. This study’s results are relevant to engineers and researchers working on soil stabilization projects, such as foundations and backfills. They can contribute to the development of sustainable and cost-effective soil stabilization solutions. Full article

(This article belongs to the Special Issue Recent Research on Tunneling and Underground Engineering)

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<p>Grain size distribution of the studied soil.</p> Full article ">Figure 2
<p>The process of the specimens; (<b>a</b>) cylindrical sample and (<b>b</b>) samples curing.</p> Full article ">Figure 3
<p>The UCS test process; (<b>a</b>) the triaxial machine, (<b>b</b>) the sample after testing.</p> Full article ">Figure 4
<p>The flowchart mechanism of the neural network.</p> Full article ">Figure 5
<p>The amended and unamended soil sample’s specific gravity characteristics.</p> Full article ">Figure 6
<p>The amended and unamended soil sample’s Atterberg limits characteristics.</p> Full article ">Figure 7
<p>The amended and unamended soil sample’s compaction characteristics.</p> Full article ">Figure 8
<p>Depiction of the maximum UCS of amended and unamended clays influenced by water content and curing time.</p> Full article ">Figure 9
<p>Depicts the UPV of amended and unamended clays influenced by water content and curing time.</p> Full article ">Figure 10
<p>Observed versus predicted values of UCS for water content, curing time, and experimental class (waste marble powder by the percentage of dry soil).</p> Full article ">Figure 11
<p>Observed versus predicted values of UPV for water content, curing time, and experimental class (waste marble powder by the percentage of dry soil).</p> Full article ">Figure 11 Cont.
<p>Observed versus predicted values of UPV for water content, curing time, and experimental class (waste marble powder by the percentage of dry soil).</p> Full article ">

14 pages, 4561 KiB

Open AccessEditor’s ChoiceArticle

Green Synthesis of Cobalt Oxide Nanoparticles Using Hyphaene thebaica Fruit Extract and Their Photocatalytic Application

by Ammara Safdar, Hamza Elsayed Ahmed Mohamed, Khaoula Hkiri, Abdul Muhaymin and Malik Maaza

Appl. Sci. 2023, 13(16), 9082; https://doi.org/10.3390/app13169082 - 9 Aug 2023

Cited by 20 | Viewed by 4635

Cobalt oxide, a multifunctional, anti-ferromagnetic p-type semiconductor with an optical bandgap of ~2.00 eV, exhibits remarkable catalytic, chemical, optical, magnetic, and electrical properties. In our study, cobalt oxide nanoparticles (Co₃O₄ NPs) were prepared by the green synthesis method using dried [...] Read more.

Cobalt oxide, a multifunctional, anti-ferromagnetic p-type semiconductor with an optical bandgap of ~2.00 eV, exhibits remarkable catalytic, chemical, optical, magnetic, and electrical properties. In our study, cobalt oxide nanoparticles (Co₃O₄ NPs) were prepared by the green synthesis method using dried fruit extracts of Hyphaene thebaica (doum palm) as a cost-effective reducing and stabilizing agent. Scanning electron microscopy (SEM) depicts stable hollow spherical entities which, consist of interconnected Co₃O₄ NPs, while energy-dispersive X-ray spectroscopy (EDS) indicates the presence of Co and O. The obtained product was identified by X-ray diffraction (XRD) that showed a sharp peak at (220), (311), (222), (400), (511) indicating the high crystallinity of the product. The Raman peaks indicate the Co₃O₄ spinel structure with an average shift of Δν~9 cm⁻¹ (191~470~510~608~675 cm⁻¹). In the Fourier transform infrared spectroscopy (FT-IR) spectrum, the major bands at 3128 cm⁻¹, 1624 cm⁻¹, 1399 cm⁻¹, 667 cm⁻¹, and 577 cm⁻¹ can be attributed to the carbonyl functional groups, amides, and Co₃O₄ NPs, respectively. The photocatalytic activity of the synthesized NPs was evaluated by degrading methylene blue dye under visible light. Approximately 93% degradation was accomplished in the reaction time of 175 min at a catalyst loading of 1 g/L under neutral pH. This study has shown that Co₃O₄ is a promising material for photocatalytic degradation. Full article

(This article belongs to the Special Issue From Plants Extracts to Nanoparticles: Synthesis, Characterization and Application)

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<p>Schematic diagram of green synthesis of Co<sub>3</sub>O<sub>4</sub> NPs.</p> Full article ">Figure 2
<p>XRD analysis of <span class="html-italic">H. thebaica</span> Co<sub>3</sub>O<sub>4</sub> NPs.</p> Full article ">Figure 3
<p>SEM images of Co<sub>3</sub>O<sub>4</sub> NPs at a magnification of (<b>a</b>) 100 kx and (<b>b</b>) 50 kx. (<b>c</b>) Histogram of Particle Size Distribution Curve.</p> Full article ">Figure 3 Cont.
<p>SEM images of Co<sub>3</sub>O<sub>4</sub> NPs at a magnification of (<b>a</b>) 100 kx and (<b>b</b>) 50 kx. (<b>c</b>) Histogram of Particle Size Distribution Curve.</p> Full article ">Figure 4
<p>TEM images of the Co<sub>3</sub>O<sub>4</sub> NPs at a resolution of (<b>a</b>) 1 μm, (<b>b</b>) 500 nm, (<b>c</b>,<b>d</b>) 200 nm, (<b>e</b>) 100 nm, and (<b>f</b>) 20 nm; (<b>g</b>) diffraction pattern.</p> Full article ">Figure 4 Cont.
<p>TEM images of the Co<sub>3</sub>O<sub>4</sub> NPs at a resolution of (<b>a</b>) 1 μm, (<b>b</b>) 500 nm, (<b>c</b>,<b>d</b>) 200 nm, (<b>e</b>) 100 nm, and (<b>f</b>) 20 nm; (<b>g</b>) diffraction pattern.</p> Full article ">Figure 5
<p>EDS spectra of Co<sub>3</sub>O<sub>4</sub> NPs.</p> Full article ">Figure 6
<p>Raman spectrum of Co<sub>3</sub>O<sub>4</sub> NPs.</p> Full article ">Figure 7
<p>FTIR spectrum of Co<sub>3</sub>O<sub>4</sub> NPs.</p> Full article ">Figure 8
<p>Evolution of UV–vis absorption spectrum of methylene blue (M.B.) under visible light irradiation using Co<sub>3</sub>O<sub>4</sub> NPs.</p> Full article ">

34 pages, 7546 KiB

Open AccessEditor’s ChoiceArticle

Debris Management in Turkey Provinces Affected by the 6 February 2023 Earthquakes: Challenges during Recovery and Potential Health and Environmental Risks

by Spyridon Mavroulis, Maria Mavrouli, Emmanuel Vassilakis, Ioannis Argyropoulos, Panayotis Carydis and Efthymis Lekkas

Appl. Sci. 2023, 13(15), 8823; https://doi.org/10.3390/app13158823 - 31 Jul 2023

Cited by 20 | Viewed by 5312

On 6 February 2023, southeastern Turkey was struck by two major earthquakes that devastated 11 provinces. Tens of thousands of buildings collapsed and more were later demolished. During post-event field surveys conducted by the authors, several disposal sites set up in the most [...] Read more.

On 6 February 2023, southeastern Turkey was struck by two major earthquakes that devastated 11 provinces. Tens of thousands of buildings collapsed and more were later demolished. During post-event field surveys conducted by the authors, several disposal sites set up in the most affected provinces were detected and checked for suitability. Based on field observations on the properties of sites and their surrounding areas as well as on the implemented debris management activities, it is concluded that all sites had characteristics that did not allow them to be classified as safe for earthquake debris management. This inadequacy is mainly attributed to their proximity to areas, where thousands of people reside. As regards the environmental impact, these sites were operating within or close to surface water bodies. This situation reveals a rush for rapid recovery resulting in serious errors in the preparation and implementation of disaster management plans. In this context, measures for effective debris management are proposed based on the existing scientific knowledge and operational experience. This paper aims to highlight challenges during earthquakes debris management and related threats posed to public health and the environment in order to be avoided in future destructive events. Full article

(This article belongs to the Special Issue Mapping, Monitoring and Assessing Disasters II)

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<p>The epicenters of the 6 February 2023 earthquakes along the East Anatolian Fault Zone (EAFZ) based on the United States Geological Survey (USGS) [<a href="#B9-applsci-13-08823" class="html-bibr">9</a>,<a href="#B10-applsci-13-08823" class="html-bibr">10</a>]. The Mw = 7.8 was generated in the main strand of the EAFZ (MSEAFZ) and the Mw = 7.5 earthquake in the northern strand of the EAFZ (NSEAFZ). 11 provinces were affected by the earthquakes with their largest cities heavily affected by the earthquake ground motion and related primary and secondary effects resulting in tens of thousands of fatalities.</p> Full article ">Figure 2
<p>Debris volumes (in million tonnes) generated by earthquakes worldwide from 1994 to 2023 against the total affected people based on the data presented in <a href="#applsci-13-08823-t002" class="html-table">Table 2</a>. The maximum estimated volume is taken into account for each seismic event. The diagram contains data from 1994 to 2023 including the 2023 Turkey-Syria earthquakes based on Xiao et al. [<a href="#B53-applsci-13-08823" class="html-bibr">53</a>] and UNDP [<a href="#B52-applsci-13-08823" class="html-bibr">52</a>].</p> Full article ">Figure 3
<p>Map depicting the locations of earthquake debris disposal sites set up in the provinces of southeastern Turkey most affected by the 6 February 2023 earthquakes. The words in italics correspond to the provinces’ names.</p> Full article ">Figure 4
<p>Pseudocolor image maps in the Samandağ disposal site area before (first map, acquisition on 3 February 2023) and after (second map, acquisition on 10 June 2023) the initiation of earthquake debris removal. Four months after the earthquake the unsorted earthquake debris pile has entirely covered the former swamp area formed north and west of the stadium.</p> Full article ">Figure 5
<p>Views of the Samandağ disposal site. Dust clouds were constantly formed (<b>a</b>,<b>b</b>) due to the limited prevention measures applied during treatment. (<b>b</b>) The pile was formed very close to a stadium where a tented camp had been set up. (<b>c</b>) Several types of debris in the pile reveal no sorting at all and cover a large part of the coastal swamp west of the stadium.</p> Full article ">Figure 6
<p>Pseudocolor image maps in the Antakya disposal site area before (first map, acquisition on 2 February 2023) and after (second map, acquisition on 10 June 2023) the 6 February 2023 earthquakes.</p> Full article ">Figure 7
<p>Typical views of the unsorted earthquake debris dumped in the Antakya site on 4 April 2023. (<b>a</b>,<b>b</b>) The area was entirely covered by water before debris dumping forming a small water body very close to a stream flowing to the Orontes River. During recovery, the site has been selected for disposal of unsorted earthquake debris (<b>c</b>,<b>d</b>). Τhe chaotic mixture contained mainly concrete and steel reinforcement bars from collapsed and demolished buildings as well as various damaged construction materials and related equipment.</p> Full article ">Figure 8
<p>Pseudocolor image maps in the Karaburçlu disposal site area before (first map acquired on 24 January 2023) and after (second map acquired on 11 June 2023) the 6 February 2023 earthquakes.</p> Full article ">Figure 9
<p>Characteristic drone views of the Karaburçlu earthquake debris disposal site during a post-event field survey conducted by the authors in the earthquake-affected area on 5 April 2023. (<b>a</b>) Drone view from west to east with the D825 state road passing east of the site. (<b>b</b>) Drone view from north to south with Karaburçlu village located at a small distance of about 100 m from the eastern boundary of the site. Dust was constantly present due to the crushing of concrete during debris treatment not only over the site but also in the wider area including the settlement. (<b>c</b>,<b>d</b>) The debris treatment in the Karaburçlu disposal site included concrete crushing and sorting of the steel. Steel balls were observed in the eastern part of the site (<b>c</b>,<b>d</b>).</p> Full article ">Figure 10
<p>Pseudocolor image maps in the Gölbaşı disposal site area before (first map, acquisition on 27 January 2023) and after (second map, acquisition on 11 June 2023) the 6 February 2023 earthquakes.</p> Full article ">Figure 11
<p>Characteristic drone views of the Gölbaşı disposal site on 2 April 2023. (<b>a</b>) View from north to south. The Gölbaşı town is located south of the site. The site has been set up next to the D.850 state road and at a former swamp area (<b>b</b>), very close to the lake. Furthermore, a stream flows at a small distance from the site into the lake (white dotted line) (<b>c</b>,<b>d</b>).</p> Full article ">Figure 12
<p>Pseudocolor image maps in the Adiyaman disposal site area before (first map, acquisition on 3 February 2023) and after (second map, acquisition on 8 June 2023) the initiation of the debris removal from the earthquake-affected area.</p> Full article ">Figure 12 Cont.
<p>Pseudocolor image maps in the Adiyaman disposal site area before (first map, acquisition on 3 February 2023) and after (second map, acquisition on 8 June 2023) the initiation of the debris removal from the earthquake-affected area.</p> Full article ">Figure 13
<p>Characteristic views of the earthquake debris management in Adiyaman disposal site. (<b>a</b>) The formation of dust clouds was constant during debris treatment at the site. The applied protection measures against dust-containing asbestos fibers were limited. (<b>b</b>) The debris disposal site was set up close to water bodies including a stream and an artificial channel.</p> Full article ">Figure 14
<p>Pseudocolor image maps in the Kahramanmaraş area before (<b>left</b> column, acquisition on 2 February 2023) and after (<b>right</b> column, acquisition on 11 June 2023) the 6 February 2023 earthquakes. The maps in the third row present the container camp set up at a small distance from the eastern part of the site. Τhe capacity of the camp is approximately 3000 people.</p> Full article ">Figure 15
<p>Characteristic views of the Kahramanmaras disposal site. The formation of dust clouds has the potential to affect not only the workers and volunteers on the site, but also people living in nearby container camps (<b>a</b>,<b>b</b>) for the accommodation of the earthquake-affected and homeless people among other residential areas around the site.</p> Full article ">

24 pages, 14012 KiB

Open AccessEditor’s ChoiceArticle

Operational Performance and Energy Efficiency of MEX 3D Printing with Polyamide 6 (PA6): Multi-Objective Optimization of Seven Control Settings Supported by L27 Robust Design

by Constantine David, Dimitrios Sagris, Markos Petousis, Nektarios K. Nasikas, Amalia Moutsopoulou, Evangelos Sfakiotakis, Nikolaos Mountakis, Chrysa Charou and Nectarios Vidakis

Appl. Sci. 2023, 13(15), 8819; https://doi.org/10.3390/app13158819 - 30 Jul 2023

Cited by 20 | Viewed by 2666

Both energy efficiency and robustness are popular demands for 3D-printed components nowadays. These opposing factors require compromises. This study examines the effects of seven general control variables on the energy demands and the compressive responses of polyamide (PA6) material extrusion (MEX) 3D printed [...] Read more.

Both energy efficiency and robustness are popular demands for 3D-printed components nowadays. These opposing factors require compromises. This study examines the effects of seven general control variables on the energy demands and the compressive responses of polyamide (PA6) material extrusion (MEX) 3D printed samples. Nozzle Temperature, Layer Thickness, Orientation Angle, Raster Deposition Angle, Printing Speed, Bed Temperature, and Infill Density were studied. An L27 orthogonal array was compiled with five replicas. A total of 135 trials were conducted, following the ASTM D695-02a specifications. The stopwatch method was used to assess the construction time and energy usage. The compressive strength, toughness, and elasticity modulus were experimentally determined. The Taguchi technique ranks each control parameter’s impact on each response measure. The control parameter that had the greatest impact on both energy use and printing time was layer thickness. Additionally, the infill density had the greatest influence on the compressive strength. Quadratic regression model equations were formed for each of the response measures. The ideal compromise between mechanical strength and energy efficiency is now reported, with merit related to technological and economic benefits. Full article

(This article belongs to the Special Issue Thermal and Mechanical Properties of Advanced Polymeric and Amorphous Materials)

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Figure 1
<p>Work procedure: (<b>a</b>) methodology steps of the experiments; (<b>b</b>) experimental procedure highlights; (<b>c</b>) robust design algorithm.</p> Full article ">Figure 2
<p>Values for 3D printing parameters and the compression test sample’s geometry made in accordance with ASTM D695 specifications are shown. The right side of the figure shows (<b>a</b>) a TGA graph of the weight loss versus temperature for the particular PA6 utilized in the study and (<b>b</b>) a DSC graph.</p> Full article ">Figure 3
<p>(<b>a</b>) A 3D printed specimen’s compression test stages up to delamination failure due to buckling (Runs 19), (<b>b</b>) graphical representation and microscopic examination of the fractured surface, (<b>c</b>,<b>d</b>) Micrographs of two corresponding samples’ upper- and lower-fractured surfaces (Run 11, 12) that failed by shear sliding.</p> Full article ">Figure 4
<p>Microscopic images of specimens created using several 3D printing parameters. The specimen’s 3D printing raster deposition angle is displayed in every case. On the graphic on the left side, the white arrow indicates the surface of the sample that was captured and presented in the microscope images on the right side.</p> Full article ">Figure 5
<p>Specimen failure during compression testing.</p> Full article ">Figure 6
<p>Box plots showing the relationship between the response and the work’s control parameters: (<b>a</b>) Printing time vs. PS, LT, ORA; (<b>b</b>) Part weight versus ID, RDA, ORA; (<b>c</b>) Compressive strength versus ID, ORA, RDA; (<b>d</b>) EPC versus PS, LT, ORA.</p> Full article ">Figure 7
<p>MEP for printing time and part weight versus control settings.</p> Full article ">Figure 8
<p>MEP vs. the work’s control parameters for compressive strength and energy.</p> Full article ">Figure 9
<p>Plots of the compressive strength and the energy in relation to the work’s control factors.</p> Full article ">Figure 10
<p>Response versus Control Parameters of (<b>a</b>) Part Weight versus ID and RDA; (<b>b</b>) Compressive Strength versus ID and ORA; (<b>c</b>) Energy versus PS and LT; (<b>d</b>) Printing Time versus LT and PS; (<b>e</b>) Compressive Strength versus LT and NT; (<b>f</b>) Energy versus ORA and BT.</p> Full article ">

20 pages, 3740 KiB

Open AccessEditor’s ChoiceArticle

Variational Autoencoders for Data Augmentation in Clinical Studies

by Dimitris Papadopoulos and Vangelis D. Karalis

Appl. Sci. 2023, 13(15), 8793; https://doi.org/10.3390/app13158793 - 30 Jul 2023

Cited by 19 | Viewed by 4450

Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive. The aim of this study was to introduce a novel data [...] Read more.

Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive. The aim of this study was to introduce a novel data augmentation approach in the field of clinical trials by employing variational autoencoders (VAEs). Several forms of VAEs were developed and used for the generation of virtual subjects. Various types of VAEs were explored and employed in the production of virtual individuals, and several different scenarios were investigated. The VAE-generated data exhibited similar performance to the original data, even in cases where a small proportion of them (e.g., 30–40%) was used for the reconstruction of the generated data. Additionally, the generated data showed even higher statistical power than the original data in cases of high variability. This represents an additional advantage for the use of VAEs in situations of high variability, as they can act as noise reduction. The application of VAEs in clinical trials can be a useful tool for decreasing the required sample size and, consequently, reducing the costs and time involved. Furthermore, it aligns with ethical concerns surrounding human participation in trials. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence in Medicine and Bioinformatics)

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<p>Visual representation of a variational autoencoder. The process of encoding involves compressing data from their original space to a latent space, while the decoding process involves decompressing the data. The methodology involves the utilization of neural networks as both an encoder and a decoder, with the aim of acquiring an optimal encoding–decoding scheme through an iterative optimization process. Variational autoencoders aim to establish mapping between the input data and a probability distribution across the latent space.</p> Full article ">Figure 2
<p>Schematic representation of the analysis strategy in this study. Initially, two randomly generated datasets were generated for the test (T) and reference (R) groups. Then followed subsampling to draw parts of the original population. Finally, the variational autoencoder was applied to the subsampled data in order to produce the generated datasets. The aim of the generated datasets was to exhibit the same properties as the original data. In this study, comparisons were made among the three datasets (original vs. subsampled vs. generated), as well as between the T and R groups of all datasets.</p> Full article ">Figure 3
<p>Distribution of the generated data for both R and T groups using the “softplus” (<b>a</b>) and linear (<b>b</b>) activation functions for the output layer.</p> Full article ">Figure 4
<p>Distribution of the generated data for both the R and T groups using variational autoencoders with 100 (<b>a</b>), 500 (<b>b</b>), 1000 (<b>c</b>), 5000 (<b>d</b>), and 10,000 (<b>e</b>) epochs.</p> Full article ">Figure 5
<p>Distribution of the generated data for both the R and T groups using variational autoencoders with 2 (<b>a</b>), 3 (<b>b</b>), and 4 (<b>c</b>) hidden layers for the encoder and the decoder.</p> Full article ">Figure 6
<p>Probability of accepting equivalence between the original and the generated datasets for three levels of variability (CV): (<b>a</b>) 10%, (<b>b</b>) 20%, and (<b>c</b>) 40%. The results are shown separately for the test and reference groups, as well as the two types of activation functions (“softplus” and linear) used for the hidden layers.</p> Full article ">Figure 7
<p>Probability of accepting equivalence between the test and reference groups for the original (<b>a</b>), subsampled (<b>b</b>), and generated (<b>c</b>) datasets Three levels of variability (coefficient of variation, CV) were used: 10%, 20%, and 40%. In all cases, the “softplus” activation was used for the hidden layers, while both the test and reference groups were assumed to exhibit identical average performances.</p> Full article ">Figure 8
<p>Probability of accepting equivalence between the test and reference groups for several ratios (1, 1.10, 1.25, 1.50) of the average test (T)/reference (R) performance. The comparisons were made separately for the original (<b>a</b>), subsampled (<b>b</b>), and generated datasets by the variational autoencoder (<b>c</b>). In all cases, the “softplus” activation function was used for the hidden layers and two levels of variability (coefficient of variation, CV) were used: 10% and 20%.</p> Full article ">

38 pages, 599 KiB

Open AccessEditor’s ChoiceReview

We Do Not Anthropomorphize a Robot Based Only on Its Cover: Context Matters too!

by Marion Dubois-Sage, Baptiste Jacquet, Frank Jamet and Jean Baratgin

Appl. Sci. 2023, 13(15), 8743; https://doi.org/10.3390/app13158743 - 28 Jul 2023

Cited by 12 | Viewed by 3026

The increasing presence of robots in our society raises questions about how these objects are perceived by users. Individuals seem inclined to attribute human capabilities to robots, a phenomenon called anthropomorphism. Contrary to what intuition might suggest, these attributions vary according to different [...] Read more.

The increasing presence of robots in our society raises questions about how these objects are perceived by users. Individuals seem inclined to attribute human capabilities to robots, a phenomenon called anthropomorphism. Contrary to what intuition might suggest, these attributions vary according to different factors, not only robotic factors (related to the robot itself), but also situational factors (related to the interaction setting), and human factors (related to the user). The present review aims at synthesizing the results of the literature concerning the factors that influence anthropomorphism, in order to specify their impact on the perception of robots by individuals. A total of 134 experimental studies were included from 2002 to 2023. The mere appearance hypothesis and the SEEK (sociality, effectance, and elicited agent knowledge) theory are two theories attempting to explain anthropomorphism. According to the present review, which highlights the crucial role of contextual factors, the SEEK theory better explains the observations on the subject compared to the mere appearance hypothesis, although it does not explicitly explain all the factors involved (e.g., the autonomy of the robot). Moreover, the large methodological variability in the study of anthropomorphism makes the generalization of results complex. Recommendations are proposed for future studies. Full article

(This article belongs to the Special Issue Advanced Human-Robot Interaction)

17 pages, 9910 KiB

Open AccessEditor’s ChoiceArticle

Defect Detection in CFRP Concrete Reinforcement Using the Microwave Infrared Thermography (MIRT) Method—A Numerical Modeling and Experimental Approach

by Sam Ang Keo, Barbara Szymanik, Claire Le Roy, Franck Brachelet and Didier Defer

Appl. Sci. 2023, 13(14), 8393; https://doi.org/10.3390/app13148393 - 20 Jul 2023

Cited by 14 | Viewed by 2322

This research paper presents the application of the microwave infrared thermography (MIRT) technique for the purpose of detecting and characterizing defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. Initially, a numerical model was constructed, which consisted of a broadband pyramidal [...] Read more.

This research paper presents the application of the microwave infrared thermography (MIRT) technique for the purpose of detecting and characterizing defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. Initially, a numerical model was constructed, which consisted of a broadband pyramidal horn antenna and the specimen. The present study investigated the application of a 360 W power system that operated at a frequency of 2.4 GHz, specifically focusing on two different operational modes: continuous and modulated. The specimen being examined consisted of a solid concrete slab that was coated with an adhesive layer, which was then overlaid with a layer of CFRP. Within the adhesive layer, at the interface between the concrete and CFRP, there was a defect in the form of an air gap. The study examined three distinct scenarios: a sample without any defects, a sample with a defect positioned at the center, and a sample with a defect positioned outside the center. The subsequent stage of the investigation incorporated experimental verification of the numerical modeling results. The experiment involved the utilization of two concrete specimens reinforced using CFRP, one without any defects and the other with a defect. Numerical modeling was used in this study to analyze the phenomenon of microwave heating in complex structures. The objective was to evaluate the selected antenna geometry and determine the optimal experimental configuration. Subsequently, these findings were experimentally validated. The observations conducted during the heating phase were particularly noteworthy, as they differed from previous studies that only performed observation of the sample after the heating phase. The results show that MIRT has the potential to be utilized as a method for identifying defects in concrete structures that are reinforced with CFRP. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis, 2nd Edition)

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<p>Geometry of utilized horn antenna with dimensions (dimensions in mm).</p> Full article ">Figure 2
<p>Models of the samples: (<b>a</b>) case without the defect, (<b>b</b>) case with the defect located in the center of the sample, and (<b>c</b>) case with the defect located away from the center of the sample (dimensions in mm).</p> Full article ">Figure 3
<p>Geometry of the model simulated in COMSOL: (<b>a</b>) the view on the full model and (<b>b</b>) half of the model used in the simulations.</p> Full article ">Figure 4
<p>Specification of the boundary conditions used in the modeling process.</p> Full article ">Figure 5
<p>Diagram of the function used for simulating the modulated excitation.</p> Full article ">Figure 6
<p>Utilized mesh.</p> Full article ">Figure 7
<p>The raw results of the numerical modeling for the case of continuous excitation presented for the chosen time step (t = 100 s): (<b>a</b>) the case without the defect, (<b>b</b>) the case with the defect located away from the sample’s center, and (<b>c</b>) the defect located in the center of the sample.</p> Full article ">Figure 8
<p>The raw results of the numerical modeling for the case of modulated excitation presented for the chosen time step (t = 100 s): (<b>a</b>) the case without the defect, (<b>b</b>) the case with the defect located away from the sample’s center, and (<b>c</b>) the defect located in the center of the sample.</p> Full article ">Figure 9
<p>The results of the numerical modeling for the case of continuous excitation presented for the chosen time steps (50 s, 100 s, and 150 s): (<b>a</b>) the case without the defect, (<b>b</b>) the defect located in the center of the sample, and (<b>c</b>) the case with the defect located away from the sample’s center. The thermograms in (<b>a</b>,<b>b</b>) were reconstructed using mirror reflection.</p> Full article ">Figure 10
<p>The results of the numerical modeling for the case of modulated excitation presented for the chosen time steps (50 s, 100 s, and 150 s): (<b>a</b>) the case without the defect, (<b>b</b>) the defect located in the center of the sample, and (<b>c</b>) the case with the defect located away from the sample’s center. The thermograms in (<b>a</b>,<b>b</b>) were reconstructed using mirror reflection.</p> Full article ">Figure 11
<p>Chosen ROIs for all the simulated cases: (<b>a</b>) without the defect, (<b>b</b>) defect located centrally, and (<b>c</b>) defect away from the sample’s center.</p> Full article ">Figure 12
<p>Average temperature dynamics computed from the chosen ROI: (<b>a</b>) for continuous excitation and (<b>b</b>) for modulated excitation. In the legend, “def center” denotes the sample with the defect located in the center, “no defect” indicates the sample without the defect, and “defect no center” is the sample with the defect located outside the center.</p> Full article ">Figure 13
<p>Examined sample with a visible layer of CFRP at the concrete surface. Red rectangle indicates the defect’s position under the composite layer.</p> Full article ">Figure 14
<p>Experimental test setup with the CFRP specimen and MIRT.</p> Full article ">Figure 15
<p>Thermograms at different instants: (<b>a</b>) sample without the controlled defect and (<b>b</b>) sample with the controlled defect.</p> Full article ">Figure 16
<p>Time–temperature characteristics obtained for both samples: (<b>a</b>) sample without a controlled defect and (<b>b</b>) sample with a controlled defect.</p> Full article ">Figure 17
<p>Calculated temperature difference, which was obtained by subtracting the initial temperature distribution from subsequent thermograms, for both defective and non-defective areas.</p> Full article ">

20 pages, 3356 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data

by Ayele Tesema Chala and Richard P. Ray

Appl. Sci. 2023, 13(14), 8286; https://doi.org/10.3390/app13148286 - 18 Jul 2023

Cited by 10 | Viewed by 2898

Seismic response assessment requires reliable information about subsurface conditions, including soil shear wave velocity

(V_{s})

. To properly assess seismic response, engineers need accurate information about

V_{s}

, an essential parameter for evaluating the propagation of seismic waves. However, [...] Read more.

Seismic response assessment requires reliable information about subsurface conditions, including soil shear wave velocity

(V_{s})

. To properly assess seismic response, engineers need accurate information about

V_{s}

, an essential parameter for evaluating the propagation of seismic waves. However, measuring

V_{s}

is generally challenging due to the complex and time-consuming nature of field and laboratory tests. This study aims to predict

V_{s}

using machine learning (ML) algorithms from cone penetration test (CPT) data. The study utilized four ML algorithms, namely Random Forests (RFs), Support Vector Machine (SVM), Decision Trees (DT), and eXtreme Gradient Boosting (XGBoost), to predict

V_{s}

. These ML models were trained on 70% of the datasets, while their efficiency and generalization ability were assessed on the remaining 30%. The hyperparameters for each ML model were fine-tuned through Bayesian optimization with k-fold cross-validation techniques. The performance of each ML model was evaluated using eight different metrics, including root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (

R^{2}

), performance index (

P I

), scatter index (

S I

),

A 10 - I

, and

U_{95}

. The results demonstrated that the RF model consistently performed well across all metrics. It achieved high accuracy and the lowest level of errors, indicating superior accuracy and precision in predicting

V_{s}

. The SVM and XGBoost models also exhibited strong performance, with slightly higher error metrics compared with the RF model. However, the DT model performed poorly, with higher error rates and uncertainty in predicting

V_{s}

. Based on these results, we can conclude that the RF model is highly effective at accurately predicting

V_{s}

using CPT data with minimal input features. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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<p>Scatter plots of input features with respect to target variable.</p> Full article ">Figure 1 Cont.
<p>Scatter plots of input features with respect to target variable.</p> Full article ">Figure 2
<p>Frequency distribution of input features and the target variable.</p> Full article ">Figure 2 Cont.
<p>Frequency distribution of input features and the target variable.</p> Full article ">Figure 3
<p>Box plot of input features and target variable.</p> Full article ">Figure 3 Cont.
<p>Box plot of input features and target variable.</p> Full article ">Figure 4
<p>Table of feature correlations.</p> Full article ">Figure 5
<p>Flow diagram illustrating machine learning models used for predicting <math display="inline"><semantics><mrow><msub><mi>V</mi><mi>S</mi></msub></mrow></semantics></math>.</p> Full article ">Figure 6
<p>Sample decision tree structure illustrating the splitting criteria for predicting the <math display="inline"><semantics><mrow><msub><mi>V</mi><mi>S</mi></msub></mrow></semantics></math>.</p> Full article ">Figure 7
<p>Convergence behavior of ML models.</p> Full article ">Figure 8
<p>Scatter plot illustrating the correlation between actual <math display="inline"><semantics><mrow><msub><mi>V</mi><mi>S</mi></msub></mrow></semantics></math> and predicted <math display="inline"><semantics><mrow><msub><mi>V</mi><mi>S</mi></msub></mrow></semantics></math>.</p> Full article ">Figure 8 Cont.
<p>Scatter plot illustrating the correlation between actual <math display="inline"><semantics><mrow><msub><mi>V</mi><mi>S</mi></msub></mrow></semantics></math> and predicted <math display="inline"><semantics><mrow><msub><mi>V</mi><mi>S</mi></msub></mrow></semantics></math>.</p> Full article ">Figure 9
<p>Scatter plots and frequency distributions of residuals.</p> Full article ">Figure 9 Cont.
<p>Scatter plots and frequency distributions of residuals.</p> Full article ">Figure 10
<p>Spider plot showing the performance metrics of the different models.</p> Full article ">Figure 11
<p>Performance of ML models based on of <math display="inline"><semantics><mrow><msub><mi>U</mi><mrow><mn>95</mn><mo> </mo></mrow></msub><mo>,</mo><mo> </mo><mi>A</mi><mn>10</mn><mo>−</mo><mi>I</mi><mo>,</mo><mo> </mo><mi>P</mi><mi>I</mi><mo>,</mo><mo> </mo><mi>and</mi><mo> </mo><mi>S</mi><mi>I</mi></mrow></semantics></math> indices.</p> Full article ">Figure 12
<p>Comparison of predicted and estimated <math display="inline"><semantics><mrow><msub><mi>V</mi><mi>S</mi></msub></mrow></semantics></math> based on empirical correlation.</p> Full article ">

23 pages, 1433 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry

by Surya Sasikumar Nair, Joanna Trafiałek and Wojciech Kolanowski

Appl. Sci. 2023, 13(14), 8234; https://doi.org/10.3390/app13148234 - 15 Jul 2023

Cited by 28 | Viewed by 19806

This review aims to address the current data on edible packaging systems used in food production. The growing global population, changes in the climate and dietary patterns, and the increasing need for environmental protection, have created an increasing demand for waste-free food production. [...] Read more.

This review aims to address the current data on edible packaging systems used in food production. The growing global population, changes in the climate and dietary patterns, and the increasing need for environmental protection, have created an increasing demand for waste-free food production. The need for durable and sustainable packaging materials has become significant in order to avoid food waste and environmental pollution. Edible packaging has emerged as a promising solution to extend the shelf life of food products and reduce dependence on petroleum-based resources. This review analyzes the history, production methods, barrier properties, types, and additives of edible packaging systems. The review highlights the advantages and importance of edible packaging materials and describes how they can improve sustainability measures. The market value of edible packaging materials is expanding. Further research on and developments in edible food packaging materials are needed to increase sustainable, eco-friendly packaging practices that are significant for environmental protection and food safety. Full article

(This article belongs to the Special Issue Feature Review Papers in ‘Food Science and Technology’ Section)

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22 pages, 4410 KiB

Open AccessEditor’s ChoiceArticle

Evaluating the Influence of Sand Particle Morphology on Shear Strength: A Comparison of Experimental and Machine Learning Approaches

by Firas Daghistani and Hossam Abuel-Naga

Appl. Sci. 2023, 13(14), 8160; https://doi.org/10.3390/app13148160 - 13 Jul 2023

Cited by 13 | Viewed by 3280

Particulate materials, such as sandy soil, are everywhere in nature and form the basis for many engineering applications. The aim of this research is to investigate the particle shape, size, and gradation of sandy soil and how they relate to shear strength, which [...] Read more.

Particulate materials, such as sandy soil, are everywhere in nature and form the basis for many engineering applications. The aim of this research is to investigate the particle shape, size, and gradation of sandy soil and how they relate to shear strength, which is an essential characteristic that impacts soil stability and mechanical behaviour. This will be achieved by employing a combination of experimental methodology, which includes the use of a microscope direct shear apparatus, and machine learning techniques, namely multiple linear regression and random forest regression. The experimental findings reveal that angular-shaped sand particles enhance the shear strength characteristics compared to spherical, rounded ones. Similarly, coarser sand particles improve these characteristics compared to finer sand particles, as do well-graded particles when compared to poorly graded ones. The machine learning findings show the validity of both models in predicting shear strength when compared to the experimental results, showing high accuracy. The models are designed to predict shear strength of sand considering six input features: mean particle size, uniformity coefficient, curvature coefficient, dry density, normal stress, and particle regularity. The most important features from both models were identified. In addition, an empirical equation for calculating shear strength was developed through multiple linear regression analysis using the six features. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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<p>The coarse soil (B-sand) is sieved and separated into different containers depending on the granular size.</p> Full article ">Figure 2
<p>Sieve analysis of the used particulate materials, with (<b>a</b>) displaying the sieve analysis for sand, and (<b>b</b>) showing the sieve analysis for glass beads.</p> Full article ">Figure 3
<p>Schematic view of the pluviation technique.</p> Full article ">Figure 4
<p>Drop height versus void ratio and relative density of L-Sand.</p> Full article ">Figure 5
<p>Determining particle shape through sphericity and roundness, with diagonal dotted lines indicating consistent particle regularity <math display="inline"><semantics><mrow><msub><mrow><mi mathvariant="normal">⍴</mi></mrow><mrow><mi mathvariant="normal">r</mi></mrow></msub><mo>=</mo><mfrac><mrow><mo>(</mo><mi mathvariant="normal">R</mi><mo>+</mo><mi mathvariant="normal">S</mi><mo>)</mo></mrow><mrow><mn>2</mn></mrow></mfrac></mrow></semantics></math> [<a href="#B2-applsci-13-08160" class="html-bibr">2</a>,<a href="#B49-applsci-13-08160" class="html-bibr">49</a>].</p> Full article ">Figure 6
<p>Schematic illustration of determining the particle shape parameters: roundness, sphericity, and regularity.</p> Full article ">Figure 7
<p>Workflow of the applied machine learning algorithm.</p> Full article ">Figure 8
<p>The relationship between the mean particle size and (<b>a</b>) initial dry density and (<b>b</b>) coefficient of volume compressibility.</p> Full article ">Figure 9
<p>The mean particle sizes of different sands in relation to (<b>a</b>) specific gravity and (<b>b</b>) maximum void ratio.</p> Full article ">Figure 10
<p>Shear strength versus mean particle size for B-Sand in (<b>a</b>) a loose density state and (<b>b</b>) a dense density state.</p> Full article ">Figure 11
<p>The particle size and gradation impact on the shear strength at different normal stresses (25, 50, 100, and 200) at different densities: (<b>a</b>) loose state, and (<b>b</b>) dense state.</p> Full article ">Figure 12
<p>Multiple linear regression was performed to compare actual shear strength with predicted shear strength using (<b>a</b>) the training database, (<b>b</b>) the testing database, and (<b>c</b>) 10-fold cross-validation.</p> Full article ">Figure 13
<p>Random forest regression was performed to compare actual shear strength with predicted shear strength using (<b>a</b>) the training database, (<b>b</b>) the testing database, and (<b>c</b>) 10-fold cross-validation.</p> Full article ">Figure 14
<p>Mean particle size versus the regularity.</p> Full article ">Figure 15
<p>Shear strength versus normal stress for two different mean particle sizes of glass beads.</p> Full article ">Figure 16
<p>Comparison of active lateral earth pressure and dry density for different particle sizes of various sands under different normal stresses.</p> Full article ">Figure 17
<p>Feature importance analysis comparing multiple linear regression and random forest regression with and without 10-fold cross-validation: (<b>a</b>) MLR without 10-fold cross-validation, (<b>b</b>) RFR without 10-fold cross-validation, (<b>c</b>) MLR with 10-fold cross-validation, (<b>d</b>) RFR with 10-fold cross-validation.</p> Full article ">

14 pages, 2847 KiB

Open AccessEditor’s ChoiceArticle

Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network

by Giulia Rubiu, Marco Bologna, Michaela Cellina, Maurizio Cè, Davide Sala, Roberto Pagani, Elisa Mattavelli, Deborah Fazzini, Simona Ibba, Sergio Papa and Marco Alì

Appl. Sci. 2023, 13(13), 7947; https://doi.org/10.3390/app13137947 - 6 Jul 2023

Cited by 18 | Viewed by 6823

Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental [...] Read more.

Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental X-rays. Methods: An instance segmentation model was trained using the architecture of a Mask Region-based Convolutional Neural Network (Mask-RCNN). The data for the training, validation, and testing were taken from the Tuft dental database (1000 panoramic dental radiographs). The number of the predicted label was 52 (20 deciduous and 32 permanent). The size of the training, validation, and test sets were 760, 190, and 70 images, respectively, and the split was performed randomly. The model was trained for 300 epochs, using a batch size of 10, a base learning rate of 0.001, and a warm-up multistep learning rate scheduler (gamma = 0.1). Data augmentation was performed by changing the brightness, contrast, crop, and image size. The percentage of correctly detected teeth and Dice in the test set were used as the quality metrics for the model. Results: In the test set, the percentage of correctly classified teeth was 98.4%, while the Dice score was 0.87. For both the left mandibular central and lateral incisor permanent teeth, the Dice index result was 0.91 and the accuracy was 100%. For the permanent teeth right mandibular first molar, mandibular second molar, and third molar, the Dice indexes were 0.92, 0.93, and 0.78, respectively, with an accuracy of 100% for all three different teeth. For deciduous teeth, the Dice indexes for the right mandibular lateral incisor, right mandibular canine, and right mandibular first molar were 0.89, 0.91, and 0.85, respectively, with an accuracy of 100%. Conclusions: A successful instance segmentation model for teeth identification in panoramic dental X-ray was developed and validated. This model may help speed up and automate tasks like teeth counting and identifying specific missing teeth, improving the current clinical practice. Full article

(This article belongs to the Special Issue Advanced Materials and Technology in Dental, Oral and Maxillofacial Health)

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16 pages, 1713 KiB

Open AccessEditor’s ChoiceReview

Digital Twins: The New Frontier for Personalized Medicine?

by Michaela Cellina, Maurizio Cè, Marco Alì, Giovanni Irmici, Simona Ibba, Elena Caloro, Deborah Fazzini, Giancarlo Oliva and Sergio Papa

Appl. Sci. 2023, 13(13), 7940; https://doi.org/10.3390/app13137940 - 6 Jul 2023

Cited by 59 | Viewed by 10309

Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, [...] Read more.

Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, and physiological processes. Their application has the potential to transform patient care in the direction of increasingly personalized data-driven medicine. The use of DHTs can be integrated with digital twins of healthcare institutions to improve organizational management processes and resource allocation. By modeling the complex multi-omics interactions between genetic and environmental factors, DHTs help monitor disease progression and optimize treatment plans. Through digital simulation, DHT models enable the selection of the most appropriate molecular therapy and accurate 3D representation for precision surgical planning, together with augmented reality tools. Furthermore, they allow for the development of tailored early diagnosis protocols and new targeted drugs. Furthermore, digital twins can facilitate medical training and education. By creating virtual anatomy and physiology models, medical students can practice procedures, enhance their skills, and improve their understanding of the human body. Overall, digital twins have immense potential to revolutionize healthcare, improving patient care and outcomes, reducing costs, and enhancing medical research and education. However, challenges such as data security, data quality, and data interoperability must be addressed before the widespread adoption of digital twins in healthcare. We aim to propose a narrative review on this hot topic to provide an overview of the potential applications of digital twins to improve treatment and diagnostics, but also of the challenges related to their development and widespread diffusion. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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37 pages, 11612 KiB

Open AccessEditor’s ChoiceReview

New Trends in 4D Printing: A Critical Review

by Somayeh Vatanparast, Alberto Boschetto, Luana Bottini and Paolo Gaudenzi

Appl. Sci. 2023, 13(13), 7744; https://doi.org/10.3390/app13137744 - 30 Jun 2023

Cited by 39 | Viewed by 6870

In a variety of industries, Additive Manufacturing has revolutionized the whole design–fabrication cycle. Traditional 3D printing is typically employed to produce static components, which are not able to fulfill dynamic structural requirements and are inappropriate for applications such as soft grippers, self-assembly systems, [...] Read more.

In a variety of industries, Additive Manufacturing has revolutionized the whole design–fabrication cycle. Traditional 3D printing is typically employed to produce static components, which are not able to fulfill dynamic structural requirements and are inappropriate for applications such as soft grippers, self-assembly systems, and smart actuators. To address this limitation, an innovative technology has emerged, known as “4D printing”. It processes smart materials by using 3D printing for fabricating smart structures that can be reconfigured by applying different inputs, such as heat, humidity, magnetism, electricity, light, etc. At present, 4D printing is still a growing technology, and it presents numerous challenges regarding materials, design, simulation, fabrication processes, applied strategies, and reversibility. In this work a critical review of 4D printing technologies, materials, and applications is provided. Full article

(This article belongs to the Special Issue Recent Advances in Design of Additive Manufacturing: Materials and Production Processes)

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<p>4DP structural elements.</p> Full article ">Figure 2
<p>Distribution of reviewed articles related to 4D printing.</p> Full article ">Figure 3
<p>Schematic of shape-morphing effects: one-way (<b>a</b>), two-way (<b>b</b>), and multiway SMEs (<b>c</b>) [<a href="#B125-applsci-13-07744" class="html-bibr">125</a>].</p> Full article ">Figure 4
<p>Shape memory behavior of stimulus-responsive materials: The SME in PLA (<b>a</b>) in [<a href="#B128-applsci-13-07744" class="html-bibr">128</a>]; architected mechanical metamaterials (<b>b</b>) [<a href="#B110-applsci-13-07744" class="html-bibr">110</a>].</p> Full article ">Figure 5
<p>4D-printed cross-shape specimen route design (<b>a</b>); schematic of the heterogeneous lamination (<b>b</b>); shape transformation for different heating times (<b>c</b>) [<a href="#B14-applsci-13-07744" class="html-bibr">14</a>].</p> Full article ">Figure 6
<p>A-line design: three varieties of segment composition (<b>a</b>); eight different bending orientations (<b>b</b>); after heating, a straight line may be transformed into a helix by combining distinct bending directions for separate segments [<a href="#B57-applsci-13-07744" class="html-bibr">57</a>].</p> Full article ">Figure 7
<p>Analysis of the multi-material 4D construction before (<b>a</b>) and after (<b>b</b>) a stimulus is applied and (<b>c</b>) cross-sectional image of a bilayer element after a stimulus is applied [<a href="#B51-applsci-13-07744" class="html-bibr">51</a>].</p> Full article ">Figure 8
<p>4DP multi-materials: complex origami structure composed by several hybrid hinges (<b>a</b>) [<a href="#B22-applsci-13-07744" class="html-bibr">22</a>]; self-locking mechanism of multilateral structure (<b>b</b>) [<a href="#B107-applsci-13-07744" class="html-bibr">107</a>].</p> Full article ">Figure 9
<p>4DP multi-material: direct 4DP of structural parts with architecture-driven deformation modes (<b>a</b>) [<a href="#B151-applsci-13-07744" class="html-bibr">151</a>]; protein hydrogel cation programming and morphing through mechanochemical alterations (<b>b</b>) [<a href="#B163-applsci-13-07744" class="html-bibr">163</a>]; magnetic cantilevers sensitive to the magnetic field (<b>c</b>) [<a href="#B164-applsci-13-07744" class="html-bibr">164</a>]; shape-shifting cycle of a PLA rectangular multi-ply structure and TPU (<b>d</b>) [<a href="#B166-applsci-13-07744" class="html-bibr">166</a>].</p> Full article ">

19 pages, 1672 KiB

Open AccessEditor’s ChoiceReview

An Updated Review: Opuntia ficus indica (OFI) Chemistry and Its Diverse Applications

by Rizwan Shoukat, Marta Cappai, Giorgio Pia and Luca Pilia

Appl. Sci. 2023, 13(13), 7724; https://doi.org/10.3390/app13137724 - 29 Jun 2023

Cited by 19 | Viewed by 6455

The beneficial nutrients and biologically active ingredients extracted from plants have received great attention in the prevention and treatment of several diseases, including hypercholesterolemic, cancer, diabetes, cardiovascular disorders, hypoglycemic, hypolipidemic, edema, joint pain, weight control, eye vision problems, neuroprotective effects, and asthma. Highly [...] Read more.

The beneficial nutrients and biologically active ingredients extracted from plants have received great attention in the prevention and treatment of several diseases, including hypercholesterolemic, cancer, diabetes, cardiovascular disorders, hypoglycemic, hypolipidemic, edema, joint pain, weight control, eye vision problems, neuroprotective effects, and asthma. Highly active ingredients predominantly exist in fruit and cladodes, known as phytochemicals (rich contents of minerals, betalains, carbohydrates, vitamins, antioxidants, polyphenols, and taurine), which are renowned for their beneficial properties in relation to human health. Polyphenols are widely present in plants and have demonstrated pharmacological ability through their antimicrobial, anti-inflammatory, anti-bacterial, and antioxidant capacity, and the multi-role act of Opuntia ficus indica makes it suitable for current and future usage in cosmetics for moisturizing, skin improvement, and wound care, as healthful food for essential amino acids, as macro and micro elements for body growth, in building materials as an eco-friendly and sustainable material, as a bio-composite, and as an insulator. However, a more comprehensive understanding and extensive research on the diverse array of phytochemical properties of cactus pear are needed. This review therefore aims to gather and discuss the existing literature on the chemical composition and potential applications of cactus pear extracts, as well as highlight promising directions for future research on this valuable plant. Full article

(This article belongs to the Section Chemical and Molecular Sciences)

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16 pages, 1425 KiB

Open AccessEditor’s ChoiceReview

Green Corrosion Inhibitors Based on Plant Extracts for Metals and Alloys in Corrosive Environment: A Technological and Scientific Prospection

by Williams Raphael de Souza Morais, Jaceguai Soares da Silva, Nathalia Marcelino Pereira Queiroz, Carmen Lúcia de Paiva e Silva Zanta, Adriana Santos Ribeiro and Josealdo Tonholo

Appl. Sci. 2023, 13(13), 7482; https://doi.org/10.3390/app13137482 - 25 Jun 2023

Cited by 18 | Viewed by 7387

The use of inhibitors is one of the most efficient methods to protect metals against corrosion, which affects many sectors and generates a significant effect on the world economy. This paper presents a prospection using plant extracts as green corrosion inhibitors, aiming at [...] Read more.

The use of inhibitors is one of the most efficient methods to protect metals against corrosion, which affects many sectors and generates a significant effect on the world economy. This paper presents a prospection using plant extracts as green corrosion inhibitors, aiming at the use of environmentally friendly input. For this, the authors used scientific articles and patents, with recovery of 335 articles and 42 patents related to the subject, as the source. Most technological solutions consist of extracts prepared from leaves of interest plant species, with tests carried out in acidic corrosive environments, with carbon steel (SAE1020) being the most researched material to be protected. Among the identified technologies, some point to corrosion inhibition greater than 80%. The scientific and patent literature points to the excellent performance of these compounds added to the other data collected in the present study, indicating that the exploration of this area is on the rise and very promising. Special highlight is given to the studies and development of green inhibitors in Brazil, considering the potentialities of its high vegetable biodiversity. Full article

(This article belongs to the Special Issue Corrosion Inhibitors and Protective Coatings)

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22 pages, 4939 KiB

Open AccessEditor’s ChoiceReview

Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges

by Quanyu Wang, Jin He, Caiyun Lu, Chao Wang, Han Lin, Hanyu Yang, Hang Li and Zhengyang Wu

Appl. Sci. 2023, 13(12), 7155; https://doi.org/10.3390/app13127155 - 15 Jun 2023

Cited by 15 | Viewed by 4692

This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, [...] Read more.

This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, including wheeled tractors, tracked tractors, rice transplanters, high clearance sprays, agricultural robots, agricultural tractor–trailers, etc. The application and improvement of path-tracking control methods are summarized based on the different working scenes and types of agricultural machinery. This study explores each of these methods in terms of accuracy, stability, robustness, and disadvantages/advantages. The main challenges in the field of agricultural vehicle path tracking control are defined, and future research directions are offered based on critical reviews. This review aims to provide a reference for determining which controllers to use in path-tracking control development for an autonomous agricultural vehicle. Full article

(This article belongs to the Special Issue Feature Review Papers in Agricultural Science and Technology)

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<p>Composition of agricultural machinery automatic navigation system.</p> Full article ">Figure 2
<p>Bicycle kinematic model.</p> Full article ">Figure 3
<p>Agricultural tracked vehicle kinematic model.</p> Full article ">Figure 4
<p>Agricultural articulated vehicle kinematic model.</p> Full article ">Figure 5
<p>Tracked robot dynamic model.</p> Full article ">Figure 6
<p>Block diagram of the path tracking control system.</p> Full article ">Figure 7
<p>Tractor automatic navigation control system test platform.</p> Full article ">Figure 8
<p>Automatic navigation system for crawler-type rape seeder.</p> Full article ">Figure 9
<p>Automatic driving control system for rice seeding machine.</p> Full article ">Figure 10
<p>Orchard vehicle automatic test platform.</p> Full article ">Figure 11
<p>Test prototype and test environment.</p> Full article ">Figure 12
<p>The tractor-trailer system.</p> Full article ">

18 pages, 9277 KiB

Open AccessEditor’s ChoiceArticle

Solar Sail Orbit Raising with Electro-Optically Controlled Diffractive Film

by Alessandro A. Quarta and Giovanni Mengali

Appl. Sci. 2023, 13(12), 7078; https://doi.org/10.3390/app13127078 - 13 Jun 2023

Cited by 13 | Viewed by 2240

The aim of this paper is to analyze the transfer performance of a spacecraft whose primary propulsion system is a diffractive solar sail with active, switchable panels. The spacecraft uses a propellantless thruster that converts the solar radiation pressure into propulsive acceleration by [...] Read more.

The aim of this paper is to analyze the transfer performance of a spacecraft whose primary propulsion system is a diffractive solar sail with active, switchable panels. The spacecraft uses a propellantless thruster that converts the solar radiation pressure into propulsive acceleration by taking advantage of the diffractive property of an electro-optically controlled (binary) metamaterial. The proposed analysis considers a heliocentric mission scenario where the spacecraft is required to perform a two-dimensional transfer between two concentric and coplanar circular orbits. The sail attitude is assumed to be Sun-facing, that is, with its sail nominal plane perpendicular to the incoming sunlight. This is possible since, unlike a more conventional solar sail concept that uses metalized highly reflective thin films to reflect the photons, a diffractive sail is theoretically able to generate a component of the thrust vector along the sail nominal plane also in a Sun-facing configuration. The electro-optically controlled sail film is used to change the in-plane component of the thrust vector to accomplish the transfer by minimizing the total flight time without changing the sail attitude with respect to an orbital reference frame. This work extends the mathematical model recently proposed by the authors by including the potential offered by an active control of the diffractive sail film. The paper also thoroughly analyzes the diffractive sail-based spacecraft performance in a set of classical circle-to-circle heliocentric trajectories that model transfers from Earth to Mars, Venus and Jupiter. Full article

(This article belongs to the Special Issue Recent Advances in Space Propulsion Technology)

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<p>Conceptual scheme of the thrust vector direction in a Sun-facing solar sail with a reflective or a diffractive film.</p> Full article ">Figure 2
<p>Single degree-of-freedom diffractive sail considered in the trajectory analysis discussed in Ref. [<a href="#B53-applsci-13-07078" class="html-bibr">53</a>].</p> Full article ">Figure 3
<p>Conceptual scheme of JAXA’s Interplanetary Kite-craft Accelerated by Radiation Of the Sun (IKAROS) spacecraft, showing active LCD panels to execute attitude control maneuvers.</p> Full article ">Figure 4
<p>Diffractive sail equipped with EOCPs: conceptual sketch of the thrust vector variation as a function of EOCPs state.</p> Full article ">Figure 5
<p>Propulsive acceleration vector (and its components in a body reference frame) for an ideal diffractive sail, without EOCPs, in a Sun-facing configuration.</p> Full article ">Figure 6
<p>Propulsive acceleration components as a function of <math display="inline"><semantics> <mi>τ</mi> </semantics></math> and the state of EOCPs; see also Equations (<a href="#FD2-applsci-13-07078" class="html-disp-formula">2</a>) and (<a href="#FD3-applsci-13-07078" class="html-disp-formula">3</a>).</p> Full article ">Figure 7
<p>Polar reference frame, parking orbit, and spacecraft state variables <span class="html-italic">r</span> and <math display="inline"><semantics> <mi>θ</mi> </semantics></math>.</p> Full article ">Figure 8
<p>Sequence of trajectory arcs related to the value of the adjoint variable <math display="inline"><semantics> <msub> <mi>λ</mi> <mi>v</mi> </msub> </semantics></math>.</p> Full article ">Figure 9
<p>Minimum flight time as a function of the target orbit radius when <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mspace width="0.166667em"/> <mi>mm</mi> <mo>/</mo> <msup> <mi mathvariant="normal">s</mi> <mn>2</mn> </msup> </mrow> </semantics></math>: comparison between an ideal and unconstrained reflective sail (red dashed line) and a Sun-facing diffractive sail with EOCPs (solid black line).</p> Full article ">Figure 10
<p>Force bubble of a reflective sail without attitude constraint and a diffractive sail with a Sun-facing configuration.</p> Full article ">Figure 11
<p>Final value of the polar angle as a function of the target orbit radius for an optimal transfer using an ideal and unconstrained reflective sail (red dashed line) or a Sun-facing diffractive sail with EOCPs (solid black line) when <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mspace width="0.166667em"/> <mi>mm</mi> <mo>/</mo> <msup> <mi mathvariant="normal">s</mi> <mn>2</mn> </msup> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p>Optimal transfer trajectory for the three mission scenarios. Black circle → start, black square → arrival, blue line → parking orbit, red line → target orbit, black line → optimal transfer trajectory.</p> Full article ">Figure 12 Cont.
<p>Optimal transfer trajectory for the three mission scenarios. Black circle → start, black square → arrival, blue line → parking orbit, red line → target orbit, black line → optimal transfer trajectory.</p> Full article ">Figure 13
<p>Time variation of the control variable <math display="inline"><semantics> <mi>τ</mi> </semantics></math> for the three mission cases using a Sun-facing diffractive sail. Black circle → start, black square → arrival.</p> Full article ">Figure 13 Cont.
<p>Time variation of the control variable <math display="inline"><semantics> <mi>τ</mi> </semantics></math> for the three mission cases using a Sun-facing diffractive sail. Black circle → start, black square → arrival.</p> Full article ">

31 pages, 6178 KiB

Open AccessEditor’s ChoiceReview

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part I: Bridges and Viaducts

by Edson F. Souza, Cássio Bragança, Andreia Meixedo, Diogo Ribeiro, Túlio N. Bittencourt and Hermes Carvalho

Appl. Sci. 2023, 13(12), 6940; https://doi.org/10.3390/app13126940 - 8 Jun 2023

Cited by 13 | Viewed by 2617

Bridges and viaducts are critical components of railway transport infrastructures, providing safe and efficient means for trains to cross over natural barriers such as rivers and valleys. Ensuring the continuous safe operation of these structures is therefore essential to avoid disastrous economic consequences [...] Read more.

Bridges and viaducts are critical components of railway transport infrastructures, providing safe and efficient means for trains to cross over natural barriers such as rivers and valleys. Ensuring the continuous safe operation of these structures is therefore essential to avoid disastrous economic consequences and even human losses. Drive-by methodologies have emerged as a potential and cost-effective monitoring solution for accurately and prematurely detecting damage based on instrumented vehicles while minimizing disruptions to train operations. This paper presents a critical review of drive-by methodologies applied to bridges and viaducts. Firstly, the premises of the method are briefly reviewed, and the potential applications are discussed. In sequence, several works involving the use of drive-by methodologies for modal characteristic extraction are presented, encompassing the most important methodologies developed over time as well as recent advancements in the field. Finally, the problem of damage identification is discussed—both in relation to modal and non-modal parameter-based techniques considering the most promising features and the current advancements in the development of methodologies for damage detection based on machine learning algorithms. A comprehensive conclusion is presented at the end of the article, summarizing the achievements and providing perspectives for future developments. By critically assessing the application of drive-by methodologies to bridges and viaducts, this paper contributes to the advancement of knowledge in this crucial area, emphasizing the significance of continuous monitoring for ensuring the integrity and safety of these vital transport infrastructures. Full article

(This article belongs to the Special Issue Railway Infrastructures Engineering: Latest Advances and Prospects)

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<p>Overview of the article’s layout.</p> Full article ">Figure 2
<p>Simplified vehicle bridge model [<a href="#B32-applsci-13-06940" class="html-bibr">32</a>].</p> Full article ">Figure 3
<p>Influence of suspension damping on the vehicle vertical acceleration (<math display="inline"><semantics> <mrow> <mi>ν</mi> </mrow> </semantics></math> = vehicle speed, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ω</mi> </mrow> <mrow> <mi>v</mi> </mrow> </msub> </mrow> </semantics></math> vehicle natural frequency; <math display="inline"><semantics> <mrow> <mi>L</mi> </mrow> </semantics></math> = bridge span length and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ω</mi> </mrow> <mrow> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math> bridge frequency) [<a href="#B33-applsci-13-06940" class="html-bibr">33</a>].</p> Full article ">Figure 4
<p>Flowchart of the methodology proposed by [<a href="#B23-applsci-13-06940" class="html-bibr">23</a>].</p> Full article ">Figure 5
<p>Acceleration spectra considering Class 4 irregularities (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ω</mi> </mrow> <mrow> <mi>v</mi> </mrow> </msub> </mrow> </semantics></math> vehicle natural frequency and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ω</mi> </mrow> <mrow> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math> bridge frequency): (<b>a</b>) without TSM and (<b>b</b>) with TSM [<a href="#B24-applsci-13-06940" class="html-bibr">24</a>].</p> Full article ">Figure 6
<p>Drive-by natural frequency identification of the Malahide viaduct: (<b>a</b>) overview of the train and (<b>b</b>) experimental setup [<a href="#B26-applsci-13-06940" class="html-bibr">26</a>].</p> Full article ">Figure 7
<p>Drive-by natural frequency identification of the Schmutter bridge: (<b>a</b>) experimental setup and (<b>b</b>) natural frequency results [<a href="#B27-applsci-13-06940" class="html-bibr">27</a>].</p> Full article ">Figure 8
<p>Estimated mode shapes under different bridge damping ratios: (<b>a</b>) without correction and (<b>b</b>) with correction [<a href="#B29-applsci-13-06940" class="html-bibr">29</a>].</p> Full article ">Figure 9
<p>Mode shape extraction by the Short Time Frequency Domain Decomposition technique [<a href="#B31-applsci-13-06940" class="html-bibr">31</a>].</p> Full article ">Figure 10
<p>Mode shape extraction by the crowdsourced modal identification using continuous wavelet technique: (<b>a</b>) implementation flowchart and (<b>b</b>) results [<a href="#B51-applsci-13-06940" class="html-bibr">51</a>].</p> Full article ">Figure 11
<p>Bridge damage identification approaches.</p> Full article ">Figure 12
<p>IAS results from numerical simulations of a test vehicle passing over a damaged beam: (<b>a</b>) FFT spectrum of the contact-point acceleration and (<b>b</b>) IAS amplitude [<a href="#B68-applsci-13-06940" class="html-bibr">68</a>].</p> Full article ">Figure 13
<p>Damage detection results in either the absence (<b>a</b>,<b>b</b>) or in the presence (<b>c</b>,<b>d</b>) of track irregularities with the train traveling at 100 km/h. The module of the difference in wavelet coefficients (<b>a</b>) and Hilbert spectrum (<b>b</b>) for a considered total damage on the fastening between the diagonal member and the lateral chord. The module of the difference in wavelet coefficients (<b>c</b>) and Hilbert spectrum (<b>d</b>) for a considered total damage on the fastening between the side wall diagonal member and the lateral chord [<a href="#B22-applsci-13-06940" class="html-bibr">22</a>].</p> Full article ">Figure 14
<p>AR features obtained from the simulated accelerations responses in a mid-span section of a concrete slab: (<b>a</b>) undamaged condition, (<b>b</b>) damage scenarios [<a href="#B78-applsci-13-06940" class="html-bibr">78</a>].</p> Full article ">Figure 15
<p>A sample of time-frequency power spectrogram analysis for benchmark and four positive damage scenarios [<a href="#B77-applsci-13-06940" class="html-bibr">77</a>].</p> Full article ">

16 pages, 9511 KiB

Open AccessEditor’s ChoiceArticle

Materials and Technique: The First Look at Saturnino Gatti

by Letizia Bonizzoni, Simone Caglio, Anna Galli, Luca Lanteri and Claudia Pelosi

Appl. Sci. 2023, 13(11), 6842; https://doi.org/10.3390/app13116842 - 5 Jun 2023

Cited by 13 | Viewed by 2553

As part of the study project of the pictorial cycle, attributed to Saturnino Gatti, in the church of San Panfilo at Villagrande di Tornimparte (AQ), image analyses were performed in order to document the general conservation conditions of the surfaces, and to map [...] Read more.

As part of the study project of the pictorial cycle, attributed to Saturnino Gatti, in the church of San Panfilo at Villagrande di Tornimparte (AQ), image analyses were performed in order to document the general conservation conditions of the surfaces, and to map the different painting materials to be subsequently examined using spectroscopic techniques. To acquire the images, radiation sources, ranging from ultraviolet to near infrared, were used; analyses of ultraviolet fluorescence (UVF), infrared reflectography (IRR), infrared false colors (IRFC), and optical microscopy in visible light (OM) were carried out on all the panels of the mural painting of the apsidal conch. The Hypercolorimetric Multispectral Imaging (HMI) technique was also applied in selected areas of two panels. Due to the accurate calibration system, this technique is able to obtain high-precision colorimetric and reflectance measurements, which can be repeated for proper surface monitoring. The integrated analysis of the different wavelengths’ images—in particular, the ones processed in false colors—made it possible to distinguish the portions affected by retouching or repainting and to recover the legibility of some figures that showed chromatic alterations of the original pictorial layers. The IR reflectography, in addition to highlighting the portions that lost materials and were subject to non-original interventions, emphasized the presence of the underdrawing, which was detected using the spolvero technique. UVF photography led to a preliminary mapping of the organic and inorganic materials that exhibited characteristic induced fluorescence, such as a binder in correspondence with the original azurite painting or the wide use of white zinc in the retouched areas. The collected data made it possible to form a better iconographic interpretation. Moreover, it also enabled us to accurately select the areas to be investigated using spectroscopic analyses, both in situ and on micro-samples, in order to deepen our knowledge of the techniques used by the artist to create the original painting, and to detect subsequent interventions. Full article

(This article belongs to the Special Issue Results of the II National Research Project of AIAr: Archaeometric Study of the Frescoes by Saturnino Gatti and Workshop at the Church of San Panfilo in Tornimparte (AQ, Italy))

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<p>Spectra of cut filters, A, B, and UV-IR, used in HMI and UVF.</p> Full article ">Figure 2
<p>The UVF image of panel A, Garden of Olives.</p> Full article ">Figure 3
<p>The UVF image of panel E, the Resurrection: (<b>A</b>) general view; the white rectangle indicates the detail shown in (<b>B</b>). (<b>C</b>) Detail of the golden traces found via OM in the rays.</p> Full article ">Figure 4
<p>Detail of Panel A, Garden of Olives (<b>A</b>) in visible light, (<b>B</b>) IRR, and (<b>C</b>) IRFC.</p> Full article ">Figure 5
<p>Detail of panel D, Deposition of Christ (<b>A</b>) in visible light, (<b>B</b>) IRR, and (<b>C</b>) IRFC.</p> Full article ">Figure 6
<p>The graphic user interface (GUI) of the PickViewer<sup>®</sup>, shown on the <b>left</b> of the RGB image, and on the <b>right</b>, the IRFC result is shown. Detail of panel A.</p> Full article ">Figure 7
<p>The GUI of the PickViewer<sup>®</sup> shows the RGB image on the <b>left</b>, with the selected point (white dot) for the application of the chromatic similarity tool, and on the <b>right</b>, the result is shown. Detail of panel A.</p> Full article ">Figure 8
<p>The GUI of the PickViewer<sup>®</sup> shows the RGB image on the <b>left</b>, and the PC1 obtained by applying the PCA to the three IR bands is shown on the <b>right</b>. Detail of panel A.</p> Full article ">Figure 9
<p>The GUI of the PickViewer<sup>®</sup> shows the RGB image with the selected point (white dot on the leg of the character that is lying down, probably non-original panting) for the application of the chromatic similarity tool on the <b>left</b>, and the result is shown on the <b>right</b>. Detail of panel E.</p> Full article ">Figure 10
<p>The GUI of the PickViewer<sup>®</sup> shows the RGB image with the selected point (white dot in the upper part of the green area, probably original) for the application of the chromatic similarity tool on the <b>left</b>, and the result is shown on the <b>right</b>. Detail of panel E.</p> Full article ">

12 pages, 1391 KiB

Open AccessEditor’s ChoiceArticle

Parameter Extraction of Solar Photovoltaic Model Based on Nutcracker Optimization Algorithm

by Zhenjiang Duan, Hui Yu, Qi Zhang and Li Tian

Appl. Sci. 2023, 13(11), 6710; https://doi.org/10.3390/app13116710 - 31 May 2023

Cited by 17 | Viewed by 2338

In order to improve the accuracy and reliability of the photovoltaic (PV) model, this paper explores a novel nature-inspired metaheuristic algorithm, i.e., the nutcracker optimizer algorithm (NOA), for the parameter extraction of a PV model, such as a single diode model (SDM), double [...] Read more.

In order to improve the accuracy and reliability of the photovoltaic (PV) model, this paper explores a novel nature-inspired metaheuristic algorithm, i.e., the nutcracker optimizer algorithm (NOA), for the parameter extraction of a PV model, such as a single diode model (SDM), double diode model (DDM), and triple diode model (TDM) of PV components. The Aleo Solar S79Y300 monocrystalline silicon solar panel was tested at 1000 W/m² solar irradiance and 25 °C temperature, and the results of the proposed NOA algorithm were compared with three popular algorithms, i.e., particle swarm optimization (PSO), firework algorithm (FWA), and whale optimization algorithm (WOA), in terms of algorithm accuracy and running time, and non-parametric tests were performed. The results show that the NOA can improve the efficiency of PV parameter extraction, and its performance is the best among the tested algorithms. It has the best root mean square error (RMSE) values in the SDM, being 7.92587 × 10⁻⁵ and 6.02460 × 10⁻⁵ in the DDM and 6.23617 × 10⁻⁵ in the TDM, and the shortest average execution time according to the overall ranking, making it well suited for extracting PV model parameters. Full article

(This article belongs to the Special Issue Advances in Optical and Optoelectronic Devices and Systems)

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37 pages, 6325 KiB

Open AccessEditor’s ChoiceReview

Structural Health Monitoring and Management of Cultural Heritage Structures: A State-of-the-Art Review

by Michela Rossi and Dionysios Bournas

Appl. Sci. 2023, 13(11), 6450; https://doi.org/10.3390/app13116450 - 25 May 2023

Cited by 36 | Viewed by 5829

In recent decades, the urgency to protect and upgrade cultural heritage structures (CHS) has become of primary importance due to their unique value and potential areas of impact (economic, social, cultural, and environmental). Structural health monitoring (SHM) and the management of CHS are [...] Read more.

In recent decades, the urgency to protect and upgrade cultural heritage structures (CHS) has become of primary importance due to their unique value and potential areas of impact (economic, social, cultural, and environmental). Structural health monitoring (SHM) and the management of CHS are emerging as decisive safeguard measures aimed at assessing the actual state of the conservation and integrity of the structure. Moreover, the data collected from SHM are essential to plan cost-effective and sustainable maintenance solutions, in compliance with the basic preservation principles for historic buildings, such as minimum intervention. It is evident that, compared to new buildings, the application of SHM to CHS is even more challenging because of the uniqueness of each monitored structure and the need to respect its architectural and historical value. This paper aims to present a state-of-the-art evaluation of the current traditional and innovative SHM techniques adopted for CHS and to identify future research trends. First, a general introduction regarding the use of monitoring strategies and technologies for CHS is presented. Next, various traditional SHM techniques currently used in CHS are described. Then, attention is focused on the most recent technologies, such as fibre optic sensors and smart-sensing materials. Finally, an overview of innovative methods and tools for managing and analysing SHM data, including IoT-SHM systems and the integration of BIM in heritage structures, is provided. Full article

(This article belongs to the Collection Nondestructive Testing (NDT))

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<p>Static-dynamic SHM system installed in Consoli Palace, with crack meters (LVDTs), temperature sensors (T1, T2) and uni-axial piezoelectric accelerometers (A1–A3) (modified from [<a href="#B45-applsci-13-06450" class="html-bibr">45</a>]).</p> Full article ">Figure 2
<p>Monitoring of cracks employing: (<b>a</b>) plastic Tell-Tale crack meter, and (<b>b</b>) an LVDT sensor (from [<a href="#B48-applsci-13-06450" class="html-bibr">48</a>]).</p> Full article ">Figure 3
<p>Biaxial tiltmeters installed on eight capitals of the Milan Cathedral (from [<a href="#B37-applsci-13-06450" class="html-bibr">37</a>]).</p> Full article ">Figure 4
<p>Different types of dynamic sensors: force-balance accelerometers (<b>a</b>), from [<a href="#B38-applsci-13-06450" class="html-bibr">38</a>], and MEMS accelerometers (<b>b</b>), from [<a href="#B40-applsci-13-06450" class="html-bibr">40</a>].</p> Full article ">Figure 5
<p>Typical optical fibre cross–section.</p> Full article ">Figure 6
<p>FBG operating principle.</p> Full article ">Figure 7
<p>Application of the multiaxial textile with integrated SHM sensors on a stone masonry building within the POLYMAST project (from [<a href="#B127-applsci-13-06450" class="html-bibr">127</a>]).</p> Full article ">Figure 8
<p>FBG sensors in a TRM coupon tested in tensile: configuration of FBG sensors (<b>a</b>); preparation of TRM coupon (<b>b</b>); stress versus strain curves (<b>c</b>) (from [<a href="#B129-applsci-13-06450" class="html-bibr">129</a>]).</p> Full article ">Figure 9
<p>An RFID sensor equipped with a piezoelectric sensor for crack detection ((<b>a</b>), from [<a href="#B136-applsci-13-06450" class="html-bibr">136</a>]; capacity piezoelectric sensors embedded in mortar joints (<b>b</b>), from [<a href="#B137-applsci-13-06450" class="html-bibr">137</a>]).</p> Full article ">Figure 10
<p>Concept of smart brick technology (<b>a</b>) and an example of its application for strain monitoring within a damaged masonry building using smart brick ((<b>b</b>), from [<a href="#B142-applsci-13-06450" class="html-bibr">142</a>]).</p> Full article ">Figure 11
<p>Orthophoto from close-range photogrammetry acquisition (<b>a</b>), from [<a href="#B155-applsci-13-06450" class="html-bibr">155</a>]; UAV image acquisition for marker-based structural defects monitoring (<b>b</b>), from [<a href="#B156-applsci-13-06450" class="html-bibr">156</a>].</p> Full article ">Figure 12
<p>SHM-IoT system schematic (from [<a href="#B188-applsci-13-06450" class="html-bibr">188</a>]).</p> Full article ">Figure 13
<p>Workflow towards HBIM creation and integration proposed by [<a href="#B207-applsci-13-06450" class="html-bibr">207</a>]).</p> Full article ">

16 pages, 4782 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Fire Resistance of Polymer Composites with Natural Reinforcement as Safe Construction Materials for Small Vessels

by Katarzyna Bryll, Ewelina Kostecka, Mieczysław Scheibe, Renata Dobrzyńska, Tomasz Kostecki, Wojciech Ślączka and Iga Korczyńska

Appl. Sci. 2023, 13(10), 5832; https://doi.org/10.3390/app13105832 - 9 May 2023

Cited by 9 | Viewed by 1973

In small vessels, for example, yachts, polymer–glass composites are mainly used for their construction. However, the disposal and/or recycling of composite units is very difficult. It is advisable to solve the problem of disposing of post-consumer items as soon as possible. Therefore, alternative, [...] Read more.

In small vessels, for example, yachts, polymer–glass composites are mainly used for their construction. However, the disposal and/or recycling of composite units is very difficult. It is advisable to solve the problem of disposing of post-consumer items as soon as possible. Therefore, alternative, environmentally friendly, but also durable and safe construction materials are being sought. Such materials can be polymer–natural composites, which can be used as a potential material (alternative to polymer–glass composites) for the construction of small vessels. However, its performance properties should be investigated as new construction materials. The possibility of using polymer–hemp composites was assessed in terms of safety, i.e., the fire resistance of these materials. This paper compares selected characteristics that the reaction of composite materials has to fire with glass fiber and hemp fiber reinforcements. During the study, a natural composite reinforced with hemp fabric was investigated. Based on the laboratory test, it was found that this composite showed better susceptibility to energy recycling, with a relatively small deterioration in fire resistance compared to the composite reinforced with glass fiber. This material could therefore be a potential construction material for small vessels if we consider fire resistance in terms of the safety of the vessel’s operation. Full article

(This article belongs to the Special Issue Applied Maritime Engineering and Transportation Problems 2022)

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<p>Accidents on yachts in 2014–2020 according to European Maritime Safety Agency—EMSA.</p> Full article ">Figure 2
<p>Yacht accidents in 2012–2021 [own elaboration based on [<a href="#B6-applsci-13-05832" class="html-bibr">6</a>]].</p> Full article ">Figure 3
<p>General diagram of a cone calorimeter with helium-neon laser (1), gases temperature and pressure measurement (2), soot filter (3), oxygen analyzer (4), hood (5), cone calorimeter (6), spark igniter (7), sample (8), scale (9), vertical orientation (10) [<a href="#B34-applsci-13-05832" class="html-bibr">34</a>].</p> Full article ">Figure 4
<p>Composite flammability test: GFRP: beginning (<b>a</b>) and end (<b>b</b>) of the test; HFRP: beginning (<b>c</b>) and end (<b>d</b>) of the test.</p> Full article ">Figure 5
<p>Graph of heat release intensity during sample testing (<b>a</b>) GFRP (<b>b</b>) HFRP.</p> Full article ">Figure 6
<p>Graph of CO, CO<sub>2</sub> and O<sub>2</sub> concentrations during sample testing (<b>a</b>) GFRP (<b>b</b>) HFRP.</p> Full article ">Figure 7
<p>Graphs comparing the emissions of (<b>a</b>) CO and (<b>b</b>) CO<sub>2</sub> when testing a sample of GFRP and HFRP.</p> Full article ">Figure 8
<p>Graph comparing the light attenuation caused by smoke production when testing a sample of GFRP and HFRP.</p> Full article ">

24 pages, 6441 KiB

Open AccessEditor’s ChoiceReview

How Does the Metaverse Shape Education? A Systematic Literature Review

by Fabio De Felice, Antonella Petrillo, Gianfranco Iovine, Cinzia Salzano and Ilaria Baffo

Appl. Sci. 2023, 13(9), 5682; https://doi.org/10.3390/app13095682 - 5 May 2023

Cited by 35 | Viewed by 12853

In recent years, the potential of the metaverse as a tool to connect people has been increasingly recognized. The opportunities offered by the metaverse seem enormous in many sectors and fields of application. However, on the academic side, although a growing number of [...] Read more.

In recent years, the potential of the metaverse as a tool to connect people has been increasingly recognized. The opportunities offered by the metaverse seem enormous in many sectors and fields of application. However, on the academic side, although a growing number of papers have been found to address the adoption of the metaverse, a clear overview of the solutions in place and their impact on education has been largely neglected so far. In the context of increasing challenges found with the metaverse, this review aims to investigate the role of the metaverse as tool in education. This contribution aims to address this research gap by offering a state-of-the-art analysis of the role the metaverse plays in education in relation to the future of work. The study is based on a systematic review approach performed by means of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol. The findings of this research help us to better understand the benefits, potential and risks of the metaverse as a tool for immersive and innovative learning experiences. Implications are discussed and streams for future investigation are identified. Full article

(This article belongs to the Special Issue Smart Industrial System)

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<p>Evolution of the metaverse.</p> Full article ">Figure 2
<p>Trend of technologies related to the metaverse (source Google Trends—April 2023).</p> Full article ">Figure 3
<p>Stages of review protocol (author’s elaboration).</p> Full article ">Figure 4
<p>Flow chart for document selection based on Preferred Reporting Items for Systematic Review and Meta Analyses (PRISMA).</p> Full article ">Figure 5
<p>Publications by years (source: Scopus).</p> Full article ">Figure 6
<p>Country analysis (source: Scopus).</p> Full article ">Figure 7
<p>Documents by subject area.</p> Full article ">Figure 8
<p>Most collaborative authors (source: Scopus).</p> Full article ">Figure 9
<p>Word cloud of keywords.</p> Full article ">Figure 10
<p>Co-occurrence analysis (VOSviewer).</p> Full article ">Figure 11
<p>Metaverse experience integrated with a digital twin (elaboration by the authors).</p> Full article ">Figure 12
<p>Industrial metaverse for interactive learning (elaboration by the authors).</p> Full article ">Figure 13
<p>Implications of the metaverse.</p> Full article ">Figure 14
<p>Metaverse, artificial intelligence, and education—opportunities and challenges.</p> Full article ">

17 pages, 1171 KiB

Open AccessEditor’s ChoiceReview

Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review

by José Maurício, Inês Domingues and Jorge Bernardino

Appl. Sci. 2023, 13(9), 5521; https://doi.org/10.3390/app13095521 - 28 Apr 2023

Cited by 174 | Viewed by 33661

Transformers are models that implement a mechanism of self-attention, individually weighting the importance of each part of the input data. Their use in image classification tasks is still somewhat limited since researchers have so far chosen Convolutional Neural Networks for image classification and [...] Read more.

Transformers are models that implement a mechanism of self-attention, individually weighting the importance of each part of the input data. Their use in image classification tasks is still somewhat limited since researchers have so far chosen Convolutional Neural Networks for image classification and transformers were more targeted to Natural Language Processing (NLP) tasks. Therefore, this paper presents a literature review that shows the differences between Vision Transformers (ViT) and Convolutional Neural Networks. The state of the art that used the two architectures for image classification was reviewed and an attempt was made to understand what factors may influence the performance of the two deep learning architectures based on the datasets used, image size, number of target classes (for the classification problems), hardware, and evaluated architectures and top results. The objective of this work is to identify which of the architectures is the best for image classification and under what conditions. This paper also describes the importance of the Multi-Head Attention mechanism for improving the performance of ViT in image classification. Full article

(This article belongs to the Special Issue Artificial Intelligence in Complex Networks)

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21 pages, 9696 KiB

Open AccessEditor’s ChoiceArticle

Simulating a Digital Factory and Improving Production Efficiency by Using Virtual Reality Technology

by Michal Hovanec, Peter Korba, Martin Vencel and Samer Al-Rabeei

Appl. Sci. 2023, 13(8), 5118; https://doi.org/10.3390/app13085118 - 20 Apr 2023

Cited by 19 | Viewed by 3493

The main goal of every production is an optimally set and stable production process with the lowest possible costs. Such settings can only be achieved through many years of experience or very specific research, which focuses on several critical factors. An example of [...] Read more.

The main goal of every production is an optimally set and stable production process with the lowest possible costs. Such settings can only be achieved through many years of experience or very specific research, which focuses on several critical factors. An example of such factors can be the size and use of available space or the location of the production line and the logistical location of individual production sites, which is individual for each production process. Specific research can be carried out, for example, by means of the TX Plant simulation application, which was used in the present article for the production process of making fiber from pellets. The output of this research is the effective use of the so-called “Digital factory” to make the process in the already created conditions more efficient. This was achieved by the TX Plant simulation application, resulting in a reduced production time and increasing overall productivity. An intuitive interaction with factory equipment is possible with this approach, which allows users to immerse themselves in the virtual factory environment. As a result, a layout’s efficiency of surface use, flow of martial, and ergonomics can be assessed in real time. This paper aims to demonstrate how virtual reality (VR) can be used to simulate a digital factory to aid in decision making and enhance factory efficiency. Full article

(This article belongs to the Special Issue Variability of the Usability of Advanced Innovation Technologies of BIM, AI, VR, IoT in Research and Practice)

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<p>VR Process Simulation classifications.</p> Full article ">Figure 2
<p>Virtual reality technology implemented in product design optimization.</p> Full article ">Figure 3
<p>Drawing of the hall and its individual parts [<a href="#B35-applsci-13-05118" class="html-bibr">35</a>].</p> Full article ">Figure 4
<p>Model of the production process.</p> Full article ">Figure 5
<p>Critical part of the production process.</p> Full article ">Figure 6
<p>Statistics of the simulated process.</p> Full article ">Figure 7
<p>Percentage distribution of controller activities during the production process.</p> Full article ">Figure 8
<p>Relocation of workplaces in the production hall.</p> Full article ">Figure 9
<p>Course of the simulation after the changes in the layout of workplaces.</p> Full article ">Figure 10
<p>Statistics of simulated process after applied changes.</p> Full article ">Figure 11
<p>Percentage distribution of controller activities after the change [<a href="#B35-applsci-13-05118" class="html-bibr">35</a>].</p> Full article ">Figure 12
<p>Unused space in the production hall.</p> Full article ">Figure 13
<p>Digital factory detail of view used virtual reality technology with SIEMENS PLM technology.</p> Full article ">Figure 14
<p>Virtual reality technology shows selected production process in digital factory.</p> Full article ">Figure 15
<p>Detail of view from direct position shows in virtual reality glasses.</p> Full article ">

30 pages, 1241 KiB

Open AccessEditor’s ChoiceReview

A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography

by Shinpei Okawa and Yoko Hoshi

Appl. Sci. 2023, 13(8), 5016; https://doi.org/10.3390/app13085016 - 17 Apr 2023

Cited by 11 | Viewed by 5056

Diffuse optical tomography (DOT) is a biomedical imaging modality that can reconstruct hemoglobin concentration and associated oxygen saturation by using detected light passing through a biological medium. Various clinical applications of DOT such as the diagnosis of breast cancer and functional brain imaging [...] Read more.

Diffuse optical tomography (DOT) is a biomedical imaging modality that can reconstruct hemoglobin concentration and associated oxygen saturation by using detected light passing through a biological medium. Various clinical applications of DOT such as the diagnosis of breast cancer and functional brain imaging are expected. However, it has been difficult to obtain high spatial resolution and quantification accuracy with DOT because of diffusive light propagation in biological tissues with strong scattering and absorption. In recent years, various image reconstruction algorithms have been proposed to overcome these technical problems. Moreover, with progress in related technologies, such as artificial intelligence and supercomputers, the circumstances surrounding DOT image reconstruction have changed. To support the applications of DOT image reconstruction in clinics and new entries of related technologies in DOT, we review the recent efforts in image reconstruction of DOT from the viewpoint of (i) the forward calculation process, including the radiative transfer equation and its approximations to simulate light propagation with high precision, and (ii) the optimization process, including the use of sparsity regularization and prior information to improve the spatial resolution and quantification. Full article

(This article belongs to the Special Issue Near-Infrared Optical Tomography)

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16 pages, 928 KiB

Open AccessEditor’s ChoiceArticle

HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System

by Emad Ul Haq Qazi, Muhammad Hamza Faheem and Tanveer Zia

Appl. Sci. 2023, 13(8), 4921; https://doi.org/10.3390/app13084921 - 14 Apr 2023

Cited by 53 | Viewed by 7838

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently [...] Read more.

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks. Full article

(This article belongs to the Collection Innovation in Information Security)

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21 pages, 4860 KiB

Open AccessEditor’s ChoiceArticle

A Ship Trajectory Prediction Model Based on Attention-BILSTM Optimized by the Whale Optimization Algorithm

by Hongyu Jia, Yaoyu Yang, Jintang An and Rui Fu

Appl. Sci. 2023, 13(8), 4907; https://doi.org/10.3390/app13084907 - 13 Apr 2023

Cited by 18 | Viewed by 2505

Nowadays, maritime transportation has become one of the most important ways of international trade. However, with the increase in ship transportation, the complex maritime environment has led to frequent traffic accidents, causing huge economic losses and safety hazards. For ships in maritime transportation, [...] Read more.

Nowadays, maritime transportation has become one of the most important ways of international trade. However, with the increase in ship transportation, the complex maritime environment has led to frequent traffic accidents, causing huge economic losses and safety hazards. For ships in maritime transportation, collision avoidance and route planning can be achieved by predicting the ship’s trajectory, which can give crews warning to avoid dangers. How to predict the ship’s trajectory more accurately is of great significance for risk avoidance. However, existing ship trajectory prediction models suffer from problems such as poor prediction accuracy, poor applicability, and difficult hyperparameter design. To address these issues, this paper adopts the Bidirectional Long Short-Term Memory (BILSTM) model as the base model, as it considers contextual information of time-series data more comprehensively. Meanwhile, to improve the accuracy and fitness of complex ship trajectories, this paper adds an attention mechanism to the BILSTM model to improve the weight of key information. In addition, to solve the problem of difficult hyperparameter design, this paper optimizes the hyperparameters of the Attention-BILSTM network by fusing the Whale Optimization Algorithm (WOA). In this paper, the AIS data are filtered, and the trajectory is complemented by the cubic spline interpolation method. Using the pre-processed AIS data, this WOA-Attention-BILSTM model is compared and assessed with traditional models. The results show that compared with other models, the WOA-Attention-BILSTM prediction model has high prediction accuracy, high applicability, and high stability, which provides an effective and feasible method for ship collision avoidance, maritime surveillance, and intelligent shipping. Full article

(This article belongs to the Special Issue Applied Maritime Engineering and Transportation Problems 2022)

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<p>Schematic diagram of ship trajectory pattern.</p> Full article ">Figure 2
<p>Sliding time window.</p> Full article ">Figure 3
<p>The structure of the LSTM.</p> Full article ">Figure 4
<p>The structure of the BILSTM.</p> Full article ">Figure 5
<p>Algorithm flowchart of WOA.</p> Full article ">Figure 6
<p>The structure of the Attention mechanism.</p> Full article ">Figure 7
<p>Schematic diagram of WOA-Attention-BILSTM model.</p> Full article ">Figure 8
<p>Trajectory repaired by cubic spline interpolation.</p> Full article ">Figure 9
<p>Optimization of WOA-Attention-BILSTM with different population sizes.</p> Full article ">Figure 10
<p>The loss curve of WOA-Attention-BILSTM model.</p> Full article ">Figure 11
<p>Trajectory of the target ship. The blue lines are the trajectories of other ships in the dataset, and the red line is the target ship’s trajectory.</p> Full article ">Figure 12
<p>The visualization and comparison of the models’ prediction results.</p> Full article ">Figure 13
<p>Comparison of model prediction results by Haversine Distance.</p> Full article ">

13 pages, 490 KiB

Open AccessEditor’s ChoiceArticle

Incorporating Foreshocks in an Epidemic-like Description of Seismic Occurrence in Italy

by Giuseppe Petrillo and Eugenio Lippiello

Appl. Sci. 2023, 13(8), 4891; https://doi.org/10.3390/app13084891 - 13 Apr 2023

Cited by 10 | Viewed by 1527

The Epidemic Type Aftershock Sequence (ETAS) model is a widely used tool for cluster analysis and forecasting, owing to its ability to accurately predict aftershock occurrences. However, its capacity to explain the increase in seismic activity prior to large earthquakes—known as foreshocks—has been [...] Read more.

The Epidemic Type Aftershock Sequence (ETAS) model is a widely used tool for cluster analysis and forecasting, owing to its ability to accurately predict aftershock occurrences. However, its capacity to explain the increase in seismic activity prior to large earthquakes—known as foreshocks—has been called into question due to inconsistencies between simulated and experimental catalogs. To address this issue, we introduce a generalization of the ETAS model, called the Epidemic Type Aftershock Foreshock Sequence (ETAFS) model. This model has been shown to accurately describe seismicity in Southern California. In this study, we demonstrate that the ETAFS model is also effective in the Italian catalog, providing good agreement with the instrumental Italian catalogue (ISIDE) in terms of not only the number of aftershocks, but also the number of foreshocks—where the ETAS model fails. These findings suggest that foreshocks cannot be solely explained by cascades of triggered events, but can be reasonably considered as precursory phenomena reflecting the nucleation process of the main event. Full article

(This article belongs to the Special Issue Comprehensive Research in Earthquake Forecasting and Seismic Hazard Assessment)

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<p>Bimodal distribution of time and space components of the nearest-neighbor for the observed seismicity in Italy. Solid red line corresponds to <math display="inline"><semantics> <mrow> <mi>l</mi> <mi>o</mi> <msub> <mi>g</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>l</mi> <mi>o</mi> <msub> <mi>g</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mi>τ</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>. The fractal dimensionality is fixed at <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math>.</p> Full article ">Figure 2
<p>Instrumental magnitude distribution <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>(</mo> <mi>m</mi> <mo>)</mo> </mrow> </semantics></math> for the Italian seismicity. Black circles represents only the background activity whereas red squares the whole catalog.</p> Full article ">Figure 3
<p>Flowchart of the method employed in the study.</p> Full article ">Figure 4
<p>The fraction of numerical subsets <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>(</mo> <mi>ψ</mi> <mo>)</mo> </mrow> </semantics></math> of the ETAFS (ETASI) and ETAS simulated catalogue, with a number of aftershocks <math display="inline"><semantics> <mrow> <mfrac> <mrow> <msubsup> <mi>n</mi> <mrow> <mi>A</mi> <mo>,</mo> <mi>j</mi> </mrow> <mi>X</mi> </msubsup> <mrow> <mo>(</mo> <mi>T</mi> <mo>,</mo> <mi>R</mi> <mo>,</mo> <msub> <mi>m</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>m</mi> <mi>M</mi> </msub> <mo>)</mo> </mrow> <mo>−</mo> <msub> <mi>n</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>T</mi> <mo>,</mo> <mi>R</mi> <mo>,</mo> <msub> <mi>m</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>m</mi> <mi>M</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <msubsup> <mi>n</mi> <mrow> <mi>A</mi> <mo>,</mo> <mi>j</mi> </mrow> <mi>X</mi> </msubsup> <mrow> <mo>(</mo> <mi>T</mi> <mo>,</mo> <mi>R</mi> <mo>,</mo> <msub> <mi>m</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>m</mi> <mi>M</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>n</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>T</mi> <mo>,</mo> <mi>R</mi> <mo>,</mo> <msub> <mi>m</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>m</mi> <mi>M</mi> </msub> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>=</mo> <mi>ψ</mi> <mo>±</mo> <mn>0.005</mn> </mrow> </semantics></math>. Results are for different values of the time window <span class="html-italic">T</span>. Moving horizontally, from left to right, <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mn>2.5</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>3.5</mn> </mrow> </semantics></math> whereas vertically, from top to bottom, <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mi>M</mi> </msub> <mo>=</mo> <mn>4</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>6</mn> </mrow> </semantics></math>. The orange dashed line indicate the optimal description of the seismicity <math display="inline"><semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>The fraction of numerical subsets <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>(</mo> <mi>ψ</mi> <mo>)</mo> </mrow> </semantics></math> of the ETAS and ETAFS simulated catalogue, with a number of foreshocks <math display="inline"><semantics> <mrow> <mfrac> <mrow> <msubsup> <mi>n</mi> <mrow> <mi>F</mi> <mo>,</mo> <mi>j</mi> </mrow> <mi>X</mi> </msubsup> <mrow> <mo>(</mo> <mi>T</mi> <mo>,</mo> <mi>R</mi> <mo>,</mo> <msub> <mi>m</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>m</mi> <mi>M</mi> </msub> <mo>)</mo> </mrow> <mo>−</mo> <msub> <mi>n</mi> <mi>F</mi> </msub> <mrow> <mo>(</mo> <mi>T</mi> <mo>,</mo> <mi>R</mi> <mo>,</mo> <msub> <mi>m</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>m</mi> <mi>M</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <msubsup> <mi>n</mi> <mrow> <mi>F</mi> <mo>,</mo> <mi>j</mi> </mrow> <mi>X</mi> </msubsup> <mrow> <mo>(</mo> <mi>T</mi> <mo>,</mo> <mi>R</mi> <mo>,</mo> <msub> <mi>m</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>m</mi> <mi>M</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>n</mi> <mi>F</mi> </msub> <mrow> <mo>(</mo> <mi>T</mi> <mo>,</mo> <mi>R</mi> <mo>,</mo> <msub> <mi>m</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>m</mi> <mi>M</mi> </msub> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>=</mo> <mi>ψ</mi> <mo>±</mo> <mn>0.005</mn> </mrow> </semantics></math>. Results are for different values of time window <span class="html-italic">T</span>. Moving horizontally from left to right, <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mn>2.5</mn> <mo>,</mo> <mn>3</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>3.5</mn> </mrow> </semantics></math>, whereas from top to bottom, <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mi>M</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> and 5. The orange dashed line indicates the optimum description of the seismicity <math display="inline"><semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p> Full article ">

29 pages, 32060 KiB

Open AccessEditor’s ChoiceArticle

Study on Kinematic Structure Performance and Machining Characteristics of 3-Axis Machining Center

by Tzu-Chi Chan, Chia-Chuan Chang, Aman Ullah and Han-Huei Lin

Appl. Sci. 2023, 13(8), 4742; https://doi.org/10.3390/app13084742 - 10 Apr 2023

Cited by 7 | Viewed by 2689

The rigidity and natural frequency of machine tools considerably influence cutting and generate great forces when the tool is in contact with the workpiece. The poor static rigidity of these Vertical Machining Centre machines can cause deformations and destroy the workpiece. If the [...] Read more.

The rigidity and natural frequency of machine tools considerably influence cutting and generate great forces when the tool is in contact with the workpiece. The poor static rigidity of these Vertical Machining Centre machines can cause deformations and destroy the workpiece. If the natural frequency of the machines is low or close to the commonly used cutting frequency, they vibrate considerably, resulting in poor workpiece surfaces and thus shortening the lifespan of the tool. The main objective of this study was to develop an experimental technique for measuring the effect of machine tool stiffness. The static rigidity of the X-axis was found to be 2.20 kg/μm, while the first-, second-, and third-order natural frequencies were 27.3, 34.4, and 48.3 Hz, respectively. When an external force of 1000 N was applied, the Y-axis motor load was found to be approximately 2740 N-mm. In this study, the finite element method was mainly used to analyze the structure, static force, modal, frequency spectrum, and transient state of machine tools. The results of the static analysis were verified and compared to the experimental results. The analysis model and conditions were modified to ensure that the analysis results were consistent with the experimental results. Multi-body dynamics analyses were conducted by examining the force of each component and casting of the machine tools and the load of the motor during the cutting stroke. Moreover, an external force was applied to simulate the load condition of the motor when the machine tool is cutting to confirm the feed. In this study, we used topology optimization for effective structural optimization designs. The optimal conditions for topology optimization included lightweight structures, which resulted in reduced structural deformation and increased natural frequency. Full article

(This article belongs to the Special Issue Dynamic, Magnetic and Thermal Properties of Nanofluids)

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<p>Machine-tool structure design framework based on an integrated structural framework.</p> Full article ">Figure 1 Cont.
<p>Machine-tool structure design framework based on an integrated structural framework.</p> Full article ">Figure 2
<p>The mesh convergence test results of the finite element model.</p> Full article ">Figure 2 Cont.
<p>The mesh convergence test results of the finite element model.</p> Full article ">Figure 3
<p>Linear guide load direction [<a href="#B32-applsci-13-04742" class="html-bibr">32</a>].</p> Full article ">Figure 4
<p>Static analysis results.</p> Full article ">Figure 5
<p>Spectrum analysis result <span class="html-italic">X</span>-axis directions.</p> Full article ">Figure 6
<p>Transient analysis result.</p> Full article ">Figure 7
<p>Worktable location.</p> Full article ">Figure 8
<p>Schematic diagram of no load at the low point of the spindle.</p> Full article ">Figure 9
<p>Schematic diagram of the low point of the spindle with load.</p> Full article ">Figure 10
<p>Schematic diagram of no load at the midpoint of the spindle.</p> Full article ">Figure 11
<p>Schematic diagram of the load at the midpoint of the spindle.</p> Full article ">Figure 12
<p>Schematic diagram of no load at the high point of the spindle.</p> Full article ">Figure 13
<p>Schematic diagram of the load on the high point of the spindle.</p> Full article ">Figure 14
<p>Torsion analysis result.</p> Full article ">Figure 15
<p><span class="html-italic">X</span>-axis rigidity test.</p> Full article ">Figure 16
<p><span class="html-italic">X</span>-axis rigidity test results.</p> Full article ">Figure 17
<p>Experimental apparatus.</p> Full article ">Figure 18
<p>Cutting conditions.</p> Full article ">Figure 19
<p>D5-measurement result of Parameter 1.</p> Full article ">Figure 20
<p>D5 measurement result of Parameter 2.</p> Full article ">Figure 21
<p>D5 measurement result of Parameter 3.</p> Full article ">Figure 22
<p>D8 measurement result of Parameter 1.</p> Full article ">Figure 23
<p>D8 measurement result of Parameter 2.</p> Full article ">Figure 24
<p>D8 measurement result of Parameter 3.</p> Full article ">Figure 25
<p>Numerical validation of model analysis.</p> Full article ">

23 pages, 16844 KiB

Open AccessEditor’s ChoiceArticle

A Vision Detection Scheme Based on Deep Learning in a Waste Plastics Sorting System

by Shengping Wen, Yue Yuan and Jingfu Chen

Appl. Sci. 2023, 13(7), 4634; https://doi.org/10.3390/app13074634 - 6 Apr 2023

Cited by 16 | Viewed by 4598

The preliminary sorting of plastic products is a necessary step to improve the utilization of waste resources. To improve the quality and efficiency of sorting, a plastic detection scheme based on deep learning is proposed in this paper for a waste plastics sorting [...] Read more.

The preliminary sorting of plastic products is a necessary step to improve the utilization of waste resources. To improve the quality and efficiency of sorting, a plastic detection scheme based on deep learning is proposed in this paper for a waste plastics sorting system based on vision detection. In this scheme, the YOLOX (You Only Look Once) object detection model and the DeepSORT (Deep Simple Online and Realtime Tracking) multiple object tracking algorithm are improved and combined to make them more suitable for plastic sorting. For plastic detection, multiple data augmentations are combined to improve the detection effect, while BN (Batch Normalization) layer fusion and mixed precision inference are adopted to accelerate the model. For plastic tracking, the improved YOLOX is used as a detector, and the tracking effect is further improved by optimizing the deep cosine metric learning and the metric in the matching stage. Based on this, virtual detection lines are set up to filter and extract information to determine the sorted objects. The experimental results show that the scheme proposed in this paper makes full use of vision information to achieve dynamic and real-time detection of plastics. The system is effective and versatile for sorting complex objects. Full article

(This article belongs to the Section Applied Industrial Technologies)

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<p>The process of plastics detection.</p> Full article ">Figure 2
<p>Waste plastics sorting system.</p> Full article ">Figure 3
<p>The positive sample region proposal.</p> Full article ">Figure 4
<p>The effects of data augmentation: (<b>a</b>) traditional + mosaic; (<b>b</b>) traditional + mixup; (<b>c</b>) mosaic + mixup; (<b>d</b>) traditional + mosaic + mixup.</p> Full article ">Figure 5
<p>Building blocks of ShuffleNetV2: (<b>a</b>) basic unit; (<b>b</b>) downsampling unit.</p> Full article ">Figure 6
<p>The schematic diagram of <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>o</mi> <mi>U</mi> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>The case of <math display="inline"><semantics> <mrow> <mrow> <mi>I</mi> <mi>o</mi> <mi>U</mi> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>The case of equal <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>o</mi> <mi>U</mi> </mrow> </semantics></math>.</p> Full article ">Figure 9
<p>The schematic diagram of <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>I</mi> <mi>o</mi> <mi>U</mi> </mrow> </semantics></math>.</p> Full article ">Figure 10
<p>The schematic diagram of two-line detection method.</p> Full article ">Figure 11
<p>Plastic tracking.</p> Full article ">Figure 12
<p>Flow chart of plastic sorting.</p> Full article ">Figure 13
<p>The process of calculating the angle: (<b>a</b>) the cropped image; (<b>b</b>) gray scale; (<b>c</b>) canny edge extraction; (<b>d</b>) extraction of contour points; (<b>e</b>) calculation of convex hull; (<b>f</b>) minimum bounding rectangle.</p> Full article ">Figure 14
<p>The output rule of minimum bounding rectangle.</p> Full article ">Figure 15
<p>The processing of depth map: (<b>a</b>) convex hull; (<b>b</b>) mask; (<b>c</b>) image mask.</p> Full article ">Figure 16
<p>Training process of YOLOX.</p> Full article ">Figure 17
<p>Training process of feature extraction network.</p> Full article ">Figure 18
<p>The tracking effect of each test video.</p> Full article ">Figure 19
<p>Comparison of tracking effect: (<b>a</b>) YOLOX + DeepSORT; (<b>b</b>) improved YOLOX + DeepSORT.</p> Full article ">Figure 20
<p>The grasping process of the robotic arm: (<b>a</b>) initial position; (<b>b</b>) move and rotate; (<b>c</b>) pick up the object; (<b>d</b>) transfer the object; (<b>e</b>) place the object; (<b>f</b>) back to initial position.</p> Full article ">

20 pages, 2058 KiB

Open AccessEditor’s ChoiceReview

Human-Focused Digital Twin Applications for Occupational Safety and Health in Workplaces: A Brief Survey and Research Directions

by Jin-Sung Park, Dong-Gu Lee, Jesus A. Jimenez, Sung-Jin Lee and Jun-Woo Kim

Appl. Sci. 2023, 13(7), 4598; https://doi.org/10.3390/app13074598 - 5 Apr 2023

Cited by 17 | Viewed by 3956

Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently [...] Read more.

Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions. Full article

(This article belongs to the Special Issue Intelligent Digital Twins: Trends and Applications in the Human-Centered Manufacturing Context)

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<p>Overall survey procedure.</p> Full article ">Figure 2
<p>Number of research papers per year.</p> Full article ">Figure 3
<p>Proportion of research papers by region.</p> Full article ">Figure 4
<p>The five most frequent research areas.</p> Full article ">Figure 5
<p>Main objectives of existing applications.</p> Full article ">Figure 6
<p>Target industries of existing applications.</p> Full article ">Figure 7
<p>Tasks for human workers of existing applications.</p> Full article ">Figure 8
<p>Experimental environments of existing applications.</p> Full article ">Figure 9
<p>Data collection procedure of existing applications.</p> Full article ">Figure 10
<p>Visual representation of existing applications.</p> Full article ">Figure 11
<p>Feedback provided by the existing applications.</p> Full article ">Figure 12
<p>Data analysis methods and approaches.</p> Full article ">

16 pages, 5008 KiB

Open AccessEditor’s ChoiceArticle

Spatial Assessment of Soil Erosion Risk Using RUSLE Embedded in GIS Environment: A Case Study of Jhelum River Watershed

by Muhammad Waseem, Fahad Iqbal, Muhammad Humayun, Muhammad Umais Latif, Tayyaba Javed and Megersa Kebede Leta

Appl. Sci. 2023, 13(6), 3775; https://doi.org/10.3390/app13063775 - 15 Mar 2023

Cited by 13 | Viewed by 3782

The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment [...] Read more.

The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment output, especially during the monsoon season, leading to a decline in the reservoir’s storage capacity. This work assesses the soil erosion risk in the Jhelum River watershed (Azad Jammu and Kashmir (AJ&K), Pakistan) using the Revised Universal Soil Loss Equation of (RUSLE). The RUSLE components, including the conservation support or erosion control practice factor (P), soil erodibility factor (K), slope length and slope steepness factor (LS), rainfall erosivity factor (R), and crop cover factor (C), were integrated to compute soil erosion. Soil erosion risk and intensity maps were generated by computing the RUSLE parameters, which were then integrated with physical factors such as terrain units, elevation, slope, and land uses/cover to examine how these factors affect the spatial patterns of soil erosion loss. The 2021 rainfall data were utilized to compute the rainfall erosivity factor (R), and the soil erodibility (K) map was created using the world surface soil map prepared by the Food and Agriculture Organization (FAO). The slope length and slope steepness factor (LS) were generated in the highly rough terrain using Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). The analysis revealed that the primary land use in the watershed was cultivated land, accounting for 27% of the area, and slopes of 30% or higher were present across two-thirds of the watershed. By multiplying the five variables, the study determined that the annual average soil loss was 23.47 t ha⁻¹ yr⁻¹. In areas with dense mixed forest cover, soil erosion rates ranged from 0.23 t ha⁻¹ yr⁻¹ to 25 t ha⁻¹ yr⁻¹. The findings indicated that 55.18% of the research area has a low erosion risk, 18.62% has a medium erosion risk, 13.66% has a high risk, and 11.6% has a very high erosion risk. The study’s findings will provide guidelines to policy/decision makers for better management of the Mangla watershed. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

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14 pages, 1999 KiB

Open AccessEditor’s ChoiceArticle

Calculating the Limits of Detection in Laser-Induced Breakdown Spectroscopy: Not as Easy as It Might Seem

by Francesco Poggialini, Stefano Legnaioli, Beatrice Campanella, Bruno Cocciaro, Giulia Lorenzetti, Simona Raneri and Vincenzo Palleschi

Appl. Sci. 2023, 13(6), 3642; https://doi.org/10.3390/app13063642 - 13 Mar 2023

Cited by 19 | Viewed by 4253

The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (LOD) in laser-induced breakdown spectroscopy (LIBS) analytical applications. The derivation of the commonly used ‘3-sigma over slope’ rule and [...] Read more.

The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (LOD) in laser-induced breakdown spectroscopy (LIBS) analytical applications. The derivation of the commonly used ‘3-sigma over slope’ rule and its evolution towards the new official definition recently adopted by the International Union of Pure and Applied Chemistry (IUPAC) will be illustrated. Methods for extending the calculation of the LOD to LIBS multivariate analysis will also be discussed, using as an example the detection of Cu traces in cast iron samples by LIBS. Full article

(This article belongs to the Section Optics and Lasers)

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<p>Schematic representation of the histogram of the signal from the blank (gray) and from a sample (red). The red region is the probability of false negatives (<span class="html-italic">P<sub>FN</sub></span>), the gray region is the probability of false positives (<span class="html-italic">P<sub>FP</sub></span>).</p> Full article ">Figure 2
<p>Detail of the <span class="html-italic">LIBS</span> spectrum of the ten cast iron samples, showing the two most intense Cu lines (average spectra). The spectra are shifted on the vertical axis for making easier the comparison.</p> Full article ">Figure 3
<p>Spectral region considered for the determination of Cu in cast iron. The red areas represent the Cu signal used for univariate analysis; the gray one is the region used for estimating the background (zero) signal.</p> Full article ">Figure 4
<p>Calibration curve for copper.</p> Full article ">Figure 5
<p>Linear region of the calibration curve for Cu. The red line corresponds to the best linear fit of the Cu <span class="html-italic">LIBS</span> signal (defined as in <a href="#applsci-13-03642-f002" class="html-fig">Figure 2</a>) vs. Cu concentration.</p> Full article ">Figure 6
<p>Histograms of the blank (blue) and S9 sample Cu signal (Cu concentration = 0.15 w%, yellow). The red curves are the corresponding best fit Gaussian curves.</p> Full article ">Figure 7
<p>Histograms of the blank (blue) and S5 sample Cu signal (Cu concentration = 0.03 w%, yellow). The red curves are the corresponding best fit Gaussian curves.</p> Full article ">Figure 8
<p>Regression curve of copper in cast iron.</p> Full article ">Figure 9
<p>Distribution of the predicted values for zero concentration (black) and for a sample (yellow) at the <span class="html-italic">LOD</span> (Sample S9, Cu concentration = 0.15 w%).</p> Full article ">Figure 10
<p>Variation of the Mean Relative Error of Cu as a function of the Cu concentration.</p> Full article ">

21 pages, 5162 KiB

Open AccessEditor’s ChoiceArticle

Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis

by Vassilia J. Sinanoglou, Thalia Tsiaka, Konstantinos Aouant, Elizabeth Mouka, Georgia Ladika, Eftichia Kritsi, Spyros J. Konteles, Alexandros-George Ioannou, Panagiotis Zoumpoulakis, Irini F. Strati and Dionisis Cavouras

Appl. Sci. 2023, 13(6), 3533; https://doi.org/10.3390/app13063533 - 10 Mar 2023

Cited by 18 | Viewed by 11398

Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess [...] Read more.

Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess the quality of banana peels and flesh in terms of ripening and during storage in controlled temperatures and humidity. For this purpose, we implemented various analytical techniques, such as attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for texture, colorimetrics, and physicochemical features, along with image-analysis methods and discriminant as well as statistical analysis. Image-analysis outcomes showed that storage provoked significant degradation of banana peels based on the increased image-texture dissimilarity and the loss of the structural order of the texture. In addition, the computed features were sufficient to discriminate four ripening stages with high accuracy. Moreover, the results revealed that storage led to significant changes in the color parameters and dramatic decreases in the texture attributes of banana flesh. The combination of image and chemical analyses pinpointed that storage caused water migration to the flesh and significant starch decomposition, which was then converted into soluble sugars. The redness and yellowness of the peel; the flesh moisture content; the texture attributes; Brix; and the storage time were all strongly interrelated. The combination of these techniques, coupled with statistical tools, to monitor the physicochemical and organoleptic quality of bananas during storage could be further applied for assessing the quality of other fruits and vegetables under similar conditions. Full article

(This article belongs to the Special Issue Innovative Technologies in Food Detection)

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<p>Representative images of the banana peel over a period of 21 days (i.e., days 2, 4, 7, 9, 11, 14, 17, 21) during fruit storage and ripening.</p> Full article ">Figure 2
<p>Variations of image analysis computed features (lightness L*, a* parameter, b* parameter, mean, standard deviation, skewness, kurtosis, contrast, dissimilarity, energy, homogeneity, correlation, angular second moment (ASM), short-run emphasis (SRE), long-run emphasis (LRE), gray-level non-uniformity (GLN), run-length non-uniformity (RLN) and run percentage (RP)) of the banana peel samples, according to storage intervals of 2, 4, 7, 9, 11, 14, 17, 21 days.</p> Full article ">Figure 2 Cont.
<p>Variations of image analysis computed features (lightness L*, a* parameter, b* parameter, mean, standard deviation, skewness, kurtosis, contrast, dissimilarity, energy, homogeneity, correlation, angular second moment (ASM), short-run emphasis (SRE), long-run emphasis (LRE), gray-level non-uniformity (GLN), run-length non-uniformity (RLN) and run percentage (RP)) of the banana peel samples, according to storage intervals of 2, 4, 7, 9, 11, 14, 17, 21 days.</p> Full article ">Figure 2 Cont.
<p>Variations of image analysis computed features (lightness L*, a* parameter, b* parameter, mean, standard deviation, skewness, kurtosis, contrast, dissimilarity, energy, homogeneity, correlation, angular second moment (ASM), short-run emphasis (SRE), long-run emphasis (LRE), gray-level non-uniformity (GLN), run-length non-uniformity (RLN) and run percentage (RP)) of the banana peel samples, according to storage intervals of 2, 4, 7, 9, 11, 14, 17, 21 days.</p> Full article ">Figure 2 Cont.
<p>Variations of image analysis computed features (lightness L*, a* parameter, b* parameter, mean, standard deviation, skewness, kurtosis, contrast, dissimilarity, energy, homogeneity, correlation, angular second moment (ASM), short-run emphasis (SRE), long-run emphasis (LRE), gray-level non-uniformity (GLN), run-length non-uniformity (RLN) and run percentage (RP)) of the banana peel samples, according to storage intervals of 2, 4, 7, 9, 11, 14, 17, 21 days.</p> Full article ">Figure 3
<p>Scatter diagram of the discrimination, including textural features, amongst banana peel samples from days 2, 7, 11, and 21.</p> Full article ">Figure 4
<p>Colored images of the fruit ripening of the iodine-stained banana flesh during the 21-day storage period (i.e., days 2, 4, 7, 9, 11, 14, 17, 21).</p> Full article ">Figure 5
<p>Water activity (aw), moisture content (%), Brix, and titratable acidity (%) of the banana-flesh samples during storage at 18.0 ± 0.5 °C.</p> Full article ">Figure 6
<p>Lightness (L*), redness/greenness (a*), yellowness/blueness (b*), and hue angle (h) of the banana-flesh samples during storage at 18.0 ± 0.5 °C.</p> Full article ">Figure 6 Cont.
<p>Lightness (L*), redness/greenness (a*), yellowness/blueness (b*), and hue angle (h) of the banana-flesh samples during storage at 18.0 ± 0.5 °C.</p> Full article ">Figure 7
<p>Firmness, springiness, adhesiveness, cohesiveness, and chewiness of the banana-flesh samples during storage, at 18.0 ± 0.5 °C.</p> Full article ">Figure 8
<p>Pairwise correlation matrix between physicochemical parameters of the banana flesh and the features L*, a*, b* of the banana peel, during storage.</p> Full article ">

18 pages, 4690 KiB

Open AccessEditor’s ChoiceArticle

Effectiveness of Machine-Learning and Deep-Learning Strategies for the Classification of Heat Treatments Applied to Low-Carbon Steels Based on Microstructural Analysis

by Jorge Muñoz-Rodenas, Francisco García-Sevilla, Juana Coello-Sobrino, Alberto Martínez-Martínez and Valentín Miguel-Eguía

Appl. Sci. 2023, 13(6), 3479; https://doi.org/10.3390/app13063479 - 9 Mar 2023

Cited by 13 | Viewed by 2564

This work aims to compare the effectiveness of different machine-learning techniques for the image classification of steel microstructures. For this, we use a set of samples of hypoeutectoid steels subjected to three heat treatments: annealing, quenching and quenching with tempering. Logically, the samples [...] Read more.

This work aims to compare the effectiveness of different machine-learning techniques for the image classification of steel microstructures. For this, we use a set of samples of hypoeutectoid steels subjected to three heat treatments: annealing, quenching and quenching with tempering. Logically, the samples contain the typical constituents expected, and these are different for each treatment. Images are obtained by optical microscopy at 400× magnification and from different low-carbon steels to generate the data with some heterogeneity. Learning models are created with an image dataset for classification into three classes based on the respective heat treatments. Likewise, we develop two kinds of models by using, on the one hand, classical machine-learning methods based on the “bag of features” technique and, on the other hand, convolutional neural networks (CNN) with a transfer-learning approach by using GoogLeNet and ResNet50. We demonstrate the superiority of deep-learning techniques (CNN) over classical machine-learning methods. Full article

(This article belongs to the Special Issue Data Mining and Machine Learning in Industrial World)

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Figure 1
<p>Typical annealing microstructures of a C45E steel (<b>a</b>), a C22E steel (<b>b</b>) and a 34CrMo4 steel (<b>c</b>); globular annealing of C45E steel (<b>d</b>).</p> Full article ">Figure 2
<p>Typical quenching microstructures of C22E steel (<b>a</b>), C45E steel (<b>b</b>), 37Cr4 steel (<b>c</b>) and 40niCrMo6 steel (<b>d</b>).</p> Full article ">Figure 3
<p>Typical microstructures after quenching + tempering at 450 °C for steels C45E (<b>a</b>), 40niCrMo6 (<b>b</b>) and 37Cr4 (<b>c</b>); (<b>d</b>) corresponds to a tempering temperature of 650 °C for a C45E steel.</p> Full article ">Figure 4
<p>A selection of the 10 strongest keypoints in steel samples annealing (<b>a</b>), quenching (<b>b</b>) and tempering (<b>c</b>). 1 is the strongest keypoint and 10 the weakest.</p> Full article ">Figure 5
<p>Histograms for visual word occurrence corresponding to the images of the different heat treatments. (<b>a</b>) Original histogram representations. (<b>b</b>) Histograms sorted from the strongest to the weakest feature.</p> Full article ">Figure 6
<p>Accuracy and loss of training progress in the GoogLeNet transfer learning.</p> Full article ">Figure 7
<p>Accuracy and loss of training progress in the ResNet50 transfer learning.</p> Full article ">Figure 8
<p>Accuracy and loss of training progress in the GoogLeNet from scratch (no transfer learning).</p> Full article ">Figure 9
<p>Accuracy and loss of training progress in the ResNet50 transfer learning from scratch (no transfer learning).</p> Full article ">Figure 10
<p>Confusion matrixes for the pre-trained networks, GoogLeNet (<b>a</b>) and ResNet50 (<b>b</b>) used in transfer learning tests, i.e., corresponding to samples that were not used to train and/or validate the networks.</p> Full article ">

15 pages, 5960 KiB

Open AccessEditor’s ChoiceReview

Review of Flexible Supercapacitors Using Carbon Nanotube-Based Electrodes

by Yurim Han, Heebo Ha, Chunghyeon Choi, Hyungsub Yoon, Paolo Matteini, Jun Young Cheong and Byungil Hwang

Appl. Sci. 2023, 13(5), 3290; https://doi.org/10.3390/app13053290 - 4 Mar 2023

Cited by 26 | Viewed by 4748

Carbon nanotube (CNT)-based electrodes in flexible supercapacitors have received significant attention in recent years. Carbon nanotube fiber fabrics (CNT-FF) have emerged as promising materials due to their high surface area, excellent conductivity, and mechanical strength. Researchers have attempted to improve the energy density [...] Read more.

Carbon nanotube (CNT)-based electrodes in flexible supercapacitors have received significant attention in recent years. Carbon nanotube fiber fabrics (CNT-FF) have emerged as promising materials due to their high surface area, excellent conductivity, and mechanical strength. Researchers have attempted to improve the energy density and rate performance of CNT-FF supercapacitor electrodes through various strategies, such as functionalization with conductive materials like MnO₂ nanoparticles and/or incorporation of graphene into them. In addition, the utilization of CNTs in combination with thin metal film electrodes has also gained widespread attention. Research has focused on enhancing electrochemical performance through functionalizing CNTs with conductive materials such as graphene and metal nanoparticles, or by controlling their morphology. This review paper will discuss the recent developments in supercapacitor technology utilizing carbon nanotube-based electrodes, including CNT fiber fabrics and CNTs on thin metal film electrodes. Various strategies employed for improving energy storage performance and the strengths and weaknesses of these strategies will be discussed. Finally, the paper will conclude with a discussion on the challenges that need to be addressed in order to realize the full potential of carbon nanotube-based electrodes in supercapacitor technology. Full article

(This article belongs to the Special Issue Printed Function Sensors and Materials)

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<p>Main issues in flexible supercapacitors (SCs). Reproduced with permission from ref. [<a href="#B23-applsci-13-03290" class="html-bibr">23</a>]. Copyright 2015 Elsevier.</p> Full article ">Figure 2
<p>(<b>a</b>) Process diagram of the supercapacitor based on CNT-FF. Reproduced from ref. [<a href="#B12-applsci-13-03290" class="html-bibr">12</a>] under the Creative Commons Attribution 4.0 International (CC BY 4.0) License). (<b>b</b>) Schematic diagram of the manufacturing method of the CNT–MnO<sub>2</sub> supercapacitors. Reproduced from ref. [<a href="#B14-applsci-13-03290" class="html-bibr">14</a>] under the Creative Commons Attribution 4.0 International (CC BY 4.0) License).</p> Full article ">Figure 3
<p>A schematic showing the fabrication process and structure of the wearable supercapacitor (WSC). (<b>a</b>) The carbon cloth was used as a current collector. (<b>b</b>) The CNTs were loaded on the carbon cloth through filtration, where sodium dodecylbenzene sulfonate (SDBS) was used as a dispersant. (<b>c</b>) The graphene was mounted on the CNTs layer through filtration, where boron nitride nanosheets (BNNS) were used to prevent graphene from collapsing. (<b>d</b>) The as-fabricated WSC was assembled with hydrogel used as a solid electrolyte. (<b>e</b>) The schematic structure of the WSC. (<b>f</b>) The WSCs could be integrated into clothing to power wearable devices. Reproduced from ref. [<a href="#B10-applsci-13-03290" class="html-bibr">10</a>] under the Creative Commons Attribution 4.0 International (CC BY 4.0) License). (<b>g</b>) Schematic illustration for the formation of composites. Reproduced from ref. [<a href="#B1-applsci-13-03290" class="html-bibr">1</a>] under the Creative Commons Attribution 4.0 International (CC BY 4.0) License.</p> Full article ">Figure 4
<p>(<b>a</b>) Schematics of the synthesis procedure for the PANI/Gr/Ti wire electrode and symmetrical supercapacitor. Reproduced with permission from ref. [<a href="#B40-applsci-13-03290" class="html-bibr">40</a>]. Copyright 2018 Elsevier. (<b>b</b>) Schematic illustration of the preparation of Ni(OH)<sub>2</sub>/Ni–Cu/CW. (<b>c</b>) Typical digital images of the Cu-wire, Ni–Cu/CW and Ni(OH)<sub>2</sub>/Ni–Cu/CW. Reproduced with permission from ref. [<a href="#B41-applsci-13-03290" class="html-bibr">41</a>]. Copyright 2018 American Chemical Society. (<b>d</b>) Schematic illustration of the continuous fabrication of CNT films and the synthesis of the Ni(OH)<sub>2</sub>/CNTs composite and (<b>e</b>) digital photo of the obtained CNT films with length ~1.5 m. Reproduced with permission from ref. [<a href="#B2-applsci-13-03290" class="html-bibr">2</a>]. Copyright 2015 Elsevier.</p> Full article ">Figure 5
<p>(<b>a</b>–<b>d</b>) SEM images at different magnifications of (<b>a</b>,<b>b</b>) as-received Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> and (<b>c</b>,<b>d</b>) Ti<sub>3</sub>C<sub>2</sub>T<sub>X</sub>-Fe<sub>3</sub>O<sub>4</sub>-CNT. Reproduced from ref. [<a href="#B5-applsci-13-03290" class="html-bibr">5</a>] under the Creative Commons Attribution 4.0 International (CC BY 4.0) License). (<b>e</b>–<b>j</b>) The top view SEM images of (<b>e</b>) Ti<sub>3</sub>C<sub>2</sub>, (<b>f</b>) CNTs, and (<b>g</b>) Ti<sub>3</sub>C<sub>2</sub>/CNTs film. Cross section SEM images of (<b>h</b>) Ti<sub>3</sub>C<sub>2</sub>, (<b>i</b>) CNTs, and (<b>j</b>) Ti<sub>3</sub>C<sub>2</sub>/CNTs film deposited on the graphite paper. Reproduced with permission from ref. [<a href="#B13-applsci-13-03290" class="html-bibr">13</a>]. Copyright 2018 Elsevier.</p> Full article ">Figure 6
<p>(<b>A</b>) General scheme of hydrothermal synthesis procedure employed to prepare VS<sub>2,</sub> VS<sub>2</sub>-MX, and VS<sub>2</sub>-MX-CNT. (<b>B</b>) Comparison plot showing the specific capacitance of VS<sub>2</sub>-MX, VS<sub>2</sub>-MX-CNT-30, VS<sub>2</sub>-MX-CNT-50, and VS<sub>2</sub>-MX-CNT-70 samples at a current density from 0.2 A/g to 1 A/g. (<b>C</b>) Nyquist plot showing the EIS fit graphs in the circuit (see Inset). (<b>D</b>) Contribution of capacitive and diffusion-controlled areas of VS<sub>2</sub>-MX-CNT-50 at a scan rate of 20 mV/s. (<b>E</b>) Contribution of capacitive and diffusion-controlled reactions at scan rates ranging from 10 to 100 mV/s for VS<sub>2</sub>-MX-CNT-50 ternary hybrid electrode. (<b>F</b>) Comparison of MXene and VS<sub>2</sub>-MX-CNT-50 CV profiles at a scan rate of 80 mV/s to estimate the potential window. (<b>G</b>) The stability of potential window of VS<sub>2</sub>-MX-CNT-50 // MWCNT asymmetric devices from 1.6 to 1.8 V at 100 mV/s, as shown by CV curves. Reproduced with permission from ref. [<a href="#B42-applsci-13-03290" class="html-bibr">42</a>]. Copyright 2022 Wiley.</p> Full article ">Figure 7
<p>(<b>a</b>) Schematic illustration of the fabrication process of the NiCoO<sub>2</sub>@CNTs@NF integrated electrode. Reproduced with permission from ref. [<a href="#B3-applsci-13-03290" class="html-bibr">3</a>]. Copyright 2021 Elsevier. (<b>b</b>–<b>e</b>) SEM images of (<b>a</b>) pure CNT, (<b>b</b>) pure NiCoO<sub>2</sub>, and (<b>c</b>,<b>d</b>) NiCoO<sub>2</sub>@CNT. Reproduced with permission from ref. [<a href="#B9-applsci-13-03290" class="html-bibr">9</a>]. Copyright 2022 Elsevier. (<b>f</b>) Schematic illustration of the synthetic procedure for TMO@CNT hybrid materials through pre-coating CNT with sulfonated polystyrene. Reproduced from ref. [<a href="#B15-applsci-13-03290" class="html-bibr">15</a>] under the Creative Commons Attribution 4.0 International (CC BY 4.0) License.</p> Full article ">Figure 8
<p>(<b>A</b>) Schematic illustration of PUOCNT synthesis and of PUCNT/RGO fiber fabrication. (<b>B</b>,<b>C</b>) TEM images of PUOCNT. (<b>D</b>) Raman spectra, (<b>E</b>) XPS survey spectrum, (<b>F</b>) XPS C1s spectrum, and (<b>G</b>) nitrogen adsorption and desorption isotherms of CNT and PUOCNT. Morphologies and textural properties of conductive KTP sheets depending on coating carbon solutions. Reproduced with permission from ref. [<a href="#B43-applsci-13-03290" class="html-bibr">43</a>]. Copyright 2021 Wiley. SEM images of (<b>H</b>,<b>L</b>) neat KTP, (<b>I</b>,<b>M</b>) rGO on KTP, (<b>J</b>,<b>N</b>) SWNT on KTP, and (<b>K</b>,<b>O</b>) rGO/SWNT on KTP with different magnifications. (<b>P</b>) Porosity and mean pore diameter changes as function of carbon materials. (<b>Q</b>) Schematic illustration showing the structures of conductive KTP sheets as function of carbon materials. Reproduced with permission from ref. [<a href="#B44-applsci-13-03290" class="html-bibr">44</a>]. Copyright 2018 Wiley.</p> Full article ">

20 pages, 1537 KiB

Open AccessEditor’s ChoiceReview

Use of Machine Learning and Remote Sensing Techniques for Shoreline Monitoring: A Review of Recent Literature

by Chrysovalantis-Antonios D. Tsiakos and Christos Chalkias

Appl. Sci. 2023, 13(5), 3268; https://doi.org/10.3390/app13053268 - 3 Mar 2023

Cited by 31 | Viewed by 7061

Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and [...] Read more.

Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and different geomorphological features, while exhibiting different scales and spectral responses. Thus, the monitoring of changes in the coastal land classes and the extraction of coastlines/shorelines can be a challenging task. Earth Observation data and the application of spatiotemporal analysis methods can facilitate shoreline change analysis and detection. Apart from remote sensing methods, the advent of machine learning-based techniques presents an emerging trend, being capable of supporting the monitoring and modeling of coastal ecosystems at large scales. In this context, this study aims to provide a review of the relevant literature falling within the period of 2015–2022, where different machine learning approaches were applied for cases of coast-line/shoreline extraction and change analysis, and/or coastal dynamic monitoring. Particular emphasis is given on the analysis of the selected studies, including details about their performances, as well as their advantages and weaknesses, and information about the different environmental data employed. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

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<p>Categorization of reviewed papers and studies, based on their geographical scales.</p> Full article ">Figure 2
<p>Categorization of reviewed papers and studies via satellite sensor.</p> Full article ">Figure 3
<p>Schematic representation of the machine learning techniques investigated in the literature for shoreline/coastline extraction and monitoring of coastal dynamics [<a href="#B33-applsci-13-03268" class="html-bibr">33</a>,<a href="#B74-applsci-13-03268" class="html-bibr">74</a>,<a href="#B75-applsci-13-03268" class="html-bibr">75</a>,<a href="#B76-applsci-13-03268" class="html-bibr">76</a>,<a href="#B77-applsci-13-03268" class="html-bibr">77</a>,<a href="#B78-applsci-13-03268" class="html-bibr">78</a>,<a href="#B79-applsci-13-03268" class="html-bibr">79</a>,<a href="#B80-applsci-13-03268" class="html-bibr">80</a>,<a href="#B81-applsci-13-03268" class="html-bibr">81</a>,<a href="#B82-applsci-13-03268" class="html-bibr">82</a>,<a href="#B83-applsci-13-03268" class="html-bibr">83</a>,<a href="#B84-applsci-13-03268" class="html-bibr">84</a>,<a href="#B85-applsci-13-03268" class="html-bibr">85</a>,<a href="#B86-applsci-13-03268" class="html-bibr">86</a>,<a href="#B87-applsci-13-03268" class="html-bibr">87</a>,<a href="#B88-applsci-13-03268" class="html-bibr">88</a>,<a href="#B89-applsci-13-03268" class="html-bibr">89</a>,<a href="#B90-applsci-13-03268" class="html-bibr">90</a>,<a href="#B91-applsci-13-03268" class="html-bibr">91</a>,<a href="#B92-applsci-13-03268" class="html-bibr">92</a>,<a href="#B93-applsci-13-03268" class="html-bibr">93</a>,<a href="#B94-applsci-13-03268" class="html-bibr">94</a>,<a href="#B95-applsci-13-03268" class="html-bibr">95</a>,<a href="#B96-applsci-13-03268" class="html-bibr">96</a>,<a href="#B97-applsci-13-03268" class="html-bibr">97</a>,<a href="#B98-applsci-13-03268" class="html-bibr">98</a>,<a href="#B99-applsci-13-03268" class="html-bibr">99</a>,<a href="#B100-applsci-13-03268" class="html-bibr">100</a>,<a href="#B101-applsci-13-03268" class="html-bibr">101</a>,<a href="#B102-applsci-13-03268" class="html-bibr">102</a>,<a href="#B103-applsci-13-03268" class="html-bibr">103</a>,<a href="#B104-applsci-13-03268" class="html-bibr">104</a>,<a href="#B105-applsci-13-03268" class="html-bibr">105</a>,<a href="#B107-applsci-13-03268" class="html-bibr">107</a>].</p> Full article ">

34 pages, 6208 KiB

Open AccessEditor’s ChoiceArticle

Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions

by Cristian Gabriel Anghel and Cornel Ilinca

Appl. Sci. 2023, 13(5), 3055; https://doi.org/10.3390/app13053055 - 27 Feb 2023

Cited by 13 | Viewed by 2977

The study of extreme phenomena in hydrology generally involves frequency analysis and a time series analysis. In this article we provide enough mathematics to enable hydrology researchers to apply a wide range of probability distributions in frequency analyses of hydrological drought. The article [...] Read more.

The study of extreme phenomena in hydrology generally involves frequency analysis and a time series analysis. In this article we provide enough mathematics to enable hydrology researchers to apply a wide range of probability distributions in frequency analyses of hydrological drought. The article presents a hydrological drought frequency analysis methodology for the determination of minimum annual flows, annual drought durations and annual deficit volumes for exceedance probabilities common in water management. Eight statistical distributions from different families and with different numbers of parameters are used for the frequency analysis. The method of ordinary moments and the method of linear moments are used to estimate the parameters of the distributions. All the mathematical characteristics necessary for the application of the eight analyzed distributions, for the method of ordinary moments and the method of linear moments, are presented. The performance of the analyzed distributions is evaluated using relative mean error and relative absolute error. For the frequency analysis of the annual minimum flows, only distributions that have a lower bound close to the annual minimum value should be used, a defining characteristic having the asymptotic distributions at this value. A case study of hydrological drought frequency analysis is presented for the Prigor River. We believe that the use of software without the knowledge of the mathematics behind it is not beneficial for researchers in the field of technical hydrology; thus, the dissemination of mathematical methods and models is necessary. All the research was conducted within the Faculty of Hydrotechnics. Full article

(This article belongs to the Special Issue Hydrology and Water Resources)

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<p>Duration curves for Prigor river.</p> Full article ">Figure 2
<p>Determining drought volumes and durations for the Q<sub>80%</sub> threshold.</p> Full article ">Figure 3
<p>Methodological approach.</p> Full article ">Figure 4
<p>The Prigor River location—Prigor hydrometric station.</p> Full article ">Figure 5
<p>Water deficit graph for the Prigor River for the WMO reference period.</p> Full article ">Figure 6
<p>The probability distribution curves for minimum 1-day low flows.</p> Full article ">Figure 6 Cont.
<p>The probability distribution curves for minimum 1-day low flows.</p> Full article ">Figure 7
<p>The probability distribution curves for minimum 7-day low flows.</p> Full article ">Figure 8
<p>The probability distribution curves for minimum 30-day low flows.</p> Full article ">Figure 9
<p>The probability distribution curves for annual deficit volume.</p> Full article ">Figure 10
<p>The probability distribution curves for annual deficit volume.</p> Full article ">Figure A1
<p>The variation diagram of <math display="inline"><semantics> <mrow> <mi>L</mi> <msub> <mi>C</mi> <mi>s</mi> </msub> <mo>−</mo> <mi>L</mi> <msub> <mi>C</mi> <mi>k</mi> </msub> </mrow> </semantics></math>.</p> Full article ">

19 pages, 7110 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Comparative Analysis of Primary Photosynthetic Reactions Assessed by OJIP Kinetics in Three Brassica Crops after Drought and Recovery

by Jasenka Antunović Dunić, Selma Mlinarić, Iva Pavlović, Hrvoje Lepeduš and Branka Salopek-Sondi

Appl. Sci. 2023, 13(5), 3078; https://doi.org/10.3390/app13053078 - 27 Feb 2023

Cited by 17 | Viewed by 2488

Plant drought tolerance depends on adaptations of the photosynthetic apparatus to changing environments triggered by water deficit. The seedlings of three Brassica crops differing in drought sensitivity, Brassica oleracea L. var. capitata—white cabbage, Brassica oleracea L. var. acephala—kale, and Brassica rapa [...] Read more.

Plant drought tolerance depends on adaptations of the photosynthetic apparatus to changing environments triggered by water deficit. The seedlings of three Brassica crops differing in drought sensitivity, Brassica oleracea L. var. capitata—white cabbage, Brassica oleracea L. var. acephala—kale, and Brassica rapa L. var. pekinensis—Chinese cabbage, were exposed to drought by withholding water. Detailed insight into the photosynthetic machinery was carried out when the seedling reached a relative water content of about 45% and after re-watering by analyzing the OJIP kinetics. The key objective of this study was to find reliable parameters for distinguishing drought−tolerant and drought-sensitive varieties before permanent structural and functional changes in the photosynthetic apparatus occur. According to our findings, an increase in the total performance index (PI_total) and structure–function index (SFI), positive L and K bands, total driving forces (ΔDF), and drought resistance index (DRI) suggest drought tolerance. At the same time, susceptible varieties can be distinguished based on negative L and K bands, PI_total, SFI, and the density of reaction centers (RC/CS₀). Kale proved to be the most tolerant, Chinese cabbage was moderately susceptible, and white cabbage showed high sensitivity to the investigated drought stress. The genetic variation revealed among the selected Brassica crops could be used in breeding programs and high-precision crop management. Full article

(This article belongs to the Special Issue Biophysical Properties of Agricultural Crops)

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<p>Shapes and amplitudes of OJIP transient curves determined in three <span class="html-italic">Brassica</span> seedlings after exposure to drought and subsequent recovery are shown as kinetics of relative variable fluorescence Vt and as difference kinetics ΔVOP (<b>a</b>,<b>f</b>,<b>k</b>). Difference kinetics ΔVt for the individual bands L (<b>b</b>,<b>g</b>,<b>l</b>), K (<b>c</b>,<b>h</b>,<b>m</b>), H (<b>d</b>,<b>i</b>,<b>n</b>), and G (<b>e</b>,<b>j</b>,<b>o</b>) are plotted at different time ranges. The O, J, I, and P steps are indicated in V<sub>t</sub> curves.</p> Full article ">Figure 2
<p>Spider plots represent selected JIP-test parameters that characterize PSII functioning in three <span class="html-italic">Brassica</span> seedlings: Chinese cabbage (<b>a</b>), white cabbage (<b>b</b>) and kale (<b>c</b>), subjected to drought followed by recovery. Each dataset is normalized to the respective controls (watered seedlings) separately for each variety (control = 1). Asterisks (*) signify differences between the treatments and the corresponding control, while double-asterisks (**) represent significant differences between both the control and recovery at <span class="html-italic">p</span> ≤ 0.05 (ANOVA, HSD).</p> Full article ">Figure 3
<p>Chlorophyll <span class="html-italic">a</span> fluorescence parameters characterizing the PSII functioning: minimal fluorescence intensity, F<sub>0</sub> (<b>a</b>), normalized area, S<sub>m</sub> (<b>b</b>), structure – function index, SFI (<b>c</b>), fraction of inactivated OEC, V<sub>K</sub>/V<sub>J</sub> (<b>d</b>), density of reaction centers per excited cross section, RC/CS<sub>0</sub> (<b>e</b>) and overall connectivity parameter, <span class="html-italic">p</span> (<b>f</b>) measured in three <span class="html-italic">Brassica</span> seedlings subjected to drought and subsequent recovery. Normalized data are presented as the mean ± SD; <span class="html-italic">n</span> = 7; asterisk (*) represents a significant difference at <span class="html-italic">p</span> ≤ 0.05 (ANOVA, HSD).</p> Full article ">Figure 4
<p>Difference in the driving forces (ΔDF) of three <span class="html-italic">Brassica</span> seedlings after exposure to drought and subsequent recovery. Stacked columns represent differences in DFs in treated seedlings minus the corresponding control separately for each variety. Each DF is calculated by summing up their partial driving forces.</p> Full article ">Figure 5
<p>Linear model between logarithms of relative ET<sub>0</sub>/ABS and PI<sub>ABS</sub> in three <span class="html-italic">Brassica</span> seedlings: Chinese cabbage (<b>a</b>), white cabbage (<b>b</b>) and kale (<b>c</b>), subjected to drought (filled circles) and subsequent recovery (empty circles) relative to corresponding controls.</p> Full article ">Figure 6
<p>Drought resistance index (DRI) (<b>a</b>) of three <span class="html-italic">Brassica</span> seedlings subjected to drought relative to corresponding controls. Bars represent the means ± SD of seven measurements (<span class="html-italic">n</span> = 7); different letters represent significant differences at <span class="html-italic">p</span> ≤ 0.05 (ANOVA, HSD). Principal component analysis (PCA) (<b>b</b>) shows variation within and among three <span class="html-italic">Brassica</span> seedlings (blue dots) in the control (C) and after drought (D) and recovery (R) in relation to the PSII functioning parameters, performance index, quantum efficiencies, and flux ratios shown as red dots.</p> Full article ">

42 pages, 7626 KiB

Open AccessEditor’s ChoiceReview

Ceramic Matrix Composites for Aero Engine Applications—A Review

by George Karadimas and Konstantinos Salonitis

Appl. Sci. 2023, 13(5), 3017; https://doi.org/10.3390/app13053017 - 26 Feb 2023

Cited by 65 | Viewed by 22211

Ceramic matrix materials have attracted great attention from researchers and industry due to their material properties. When used in engineering systems, and especially in aero-engine applications, they can result in reduced weight, higher temperature capability, and/or reduced cooling needs, each of which increases [...] Read more.

Ceramic matrix materials have attracted great attention from researchers and industry due to their material properties. When used in engineering systems, and especially in aero-engine applications, they can result in reduced weight, higher temperature capability, and/or reduced cooling needs, each of which increases efficiency. This is where high-temperature ceramics have made considerable progress, and ceramic matrix composites (CMCs) are in the foreground. CMCs are classified into non-oxide and oxide-based ones. Both families have material types that have a high potential for use in high-temperature propulsion applications. The oxide materials discussed will focus on alumina and aluminosilicate/mullite base material families, whereas for non-oxides, carbon, silicon carbide, titanium carbide, and tungsten carbide CMC material families will be discussed and analyzed. Typical oxide-based ones are composed of an oxide fiber and oxide matrix (Ox-Ox). Some of the most common oxide subcategories are alumina, beryllia, ceria, and zirconia ceramics. On the other hand, the largest number of non-oxides are technical ceramics that are classified as inorganic, non-metallic materials. The most well-known non-oxide subcategories are carbides, borides, nitrides, and silicides. These matrix composites are used, for example, in combustion liners of gas turbine engines and exhaust nozzles. Until now, a thorough study on the available oxide and non-oxide-based CMCs for such applications has not been presented. This paper will focus on assessing a literature survey of the available oxide and non-oxide ceramic matrix composite materials in terms of mechanical and thermal properties, as well as the classification and fabrication methods of those CMCs. The available manufacturing and fabrication processes are reviewed and compared. Finally, the paper presents a research and development roadmap for increasing the maturity of these materials allowing for the wider adoption of aero-engine applications. Full article

(This article belongs to the Special Issue Processing, Properties and Applications of Composite Materials)

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Figure 1
<p>Oxide and non-oxide CMC material categories.</p> Full article ">Figure 2
<p>CMC materials research published since 1986.</p> Full article ">Figure 3
<p>Oxide vs. non-oxide CMC materials research.</p> Full article ">Figure 4
<p>Oxide CMC material categories.</p> Full article ">Figure 5
<p>Non-oxide CMC material categories.</p> Full article ">Figure 6
<p>Representation of CMC component parts in a gas turbine engine.</p> Full article ">Figure 7
<p>Comparison of compressive strength vs. specific stiffness.</p> Full article ">Figure 8
<p>(<b>Left</b>) Compressive strength and (<b>right</b>) specific stiffness of available CMCs and comparison with superalloys.</p> Full article ">Figure 9
<p>Comparison of fatigue strength vs. fracture toughness of the available CMCs.</p> Full article ">Figure 10
<p>(<b>Left</b>) Fatigue strength at 10<sup>7</sup> cycles and (<b>right</b>) fracture toughness of the available CMCs and comparison with superalloys.</p> Full article ">Figure 11
<p>Comparison of thermal expansion coefficient vs. thermal conductivity of the available CMCs.</p> Full article ">Figure 12
<p>(<b>Left</b>) Thermal expansion coefficient and (<b>right</b>) thermal conductivity of CMCs and comparison with superalloys.</p> Full article ">Figure 13
<p>Comparison of maximum service temperature vs. thermal shock resistance of available CMCs.</p> Full article ">Figure 14
<p>Comparison of (<b>left</b>) maximum service temperature and (<b>right</b>) thermal shock resistance of CMCs.</p> Full article ">Figure 15
<p>Fabrication procedure steps for CMCs.</p> Full article ">Figure 16
<p>Fabrication procedure steps for CMCs.</p> Full article ">Figure 17
<p>Embodied energy and price per unit volume of CMCs.</p> Full article ">Figure 18
<p>Interrelation of standards, databases, life expectance models, and design codes of CMCs.</p> Full article ">Figure 19
<p>CMC development process steps.</p> Full article ">Figure 20
<p>CMC requirements needed to be met for wider adoption.</p> Full article ">Figure 21
<p>CMC materials, coatings, and manufacturing processes for aero-engine applications Roadmap (the same key as <a href="#applsci-13-03017-f020" class="html-fig">Figure 20</a> applies).</p> Full article ">Figure 22
<p>CMC material performance against other composite matrices.</p> Full article ">Figure 23
<p>Technology indicators (arrows indicating trends).</p> Full article ">Figure 24
<p>Technology staircase.</p> Full article ">

24 pages, 2558 KiB

Open AccessEditor’s ChoiceArticle

Comparison of the Spreadability of Butter and Butter Substitutes

by Małgorzata Ziarno, Dorota Derewiaka, Anna Florowska and Iwona Szymańska

Appl. Sci. 2023, 13(4), 2600; https://doi.org/10.3390/app13042600 - 17 Feb 2023

Cited by 16 | Viewed by 7051

There are many types of butter, soft margarine, and blends, e.g., a mixture of butter and vegetable fats, on the market as bread spreads. Among these, butter and blends of butter with vegetable fats are very popular. The consumer’s choice of product is [...] Read more.

There are many types of butter, soft margarine, and blends, e.g., a mixture of butter and vegetable fats, on the market as bread spreads. Among these, butter and blends of butter with vegetable fats are very popular. The consumer’s choice of product is often determined by functional properties, such as texture, and the physicochemical composition of butter and butter substitutes. The aim of this study was to compare sixteen market samples of butter and butter substitutes in terms of spreadability and other selected structural (spreadability, hardness, adhesive force, and adhesiveness) and physicochemical parameters (water content, water distribution, plasma pH, color, acid value, peroxide number, saponification number, and instrumentally measured fatty acid profile) to investigate their correlation with spreadability. The parameters determined here were correlated with factors such as the type of sample, measuring temperature, and physicochemical composition. The statistical analysis revealed a very strong positive correlation between hardness and spreadability for all samples tested at 4 °C, as well as between hardness and spreadability for all samples tested 30 min after removal from the refrigerator; however, the interpretation of the results was different if the butter and butter substitute samples were subjected to a multivariate analysis separately. Full article

(This article belongs to the Special Issue Unconventional Raw Materials for Food Products)

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Figure 1

Figure 1
<p>Graphs of correlation matrix showing the relationship between the spreadability, hardness, adhesive force and adhesiveness of butter and butter substitute samples (<b>a</b>) and butter substitute samples alone (<b>b</b>) measured at different temperatures (with a confidence level of 95.0%).</p> Full article ">Figure 2
<p>Graphs of correlation matrix showing the relationship between the spreadability, selected physicochemical properties, and the color components of butter and butter substitute samples (<b>a</b>) and butter substitute samples alone (<b>b</b>) measured at different temperatures (with a confidence level of 95.0%).</p> Full article ">Figure 3
<p>Graphs of correlation matrix showing the relationship between spreadability, acid value, saponification number and SFA, MUFA, and PUFA fatty acid profile (<b>a</b>), as well as between spreadability, and the fatty acid percentage share (the percentage for each fatty acid determined) of butter samples (<b>b</b>) measured at different temperatures (with a confidence level of 95.0%).</p> Full article ">Figure 3 Cont.
<p>Graphs of correlation matrix showing the relationship between spreadability, acid value, saponification number and SFA, MUFA, and PUFA fatty acid profile (<b>a</b>), as well as between spreadability, and the fatty acid percentage share (the percentage for each fatty acid determined) of butter samples (<b>b</b>) measured at different temperatures (with a confidence level of 95.0%).</p> Full article ">

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